Method for predicting migration and evolution of characteristic components based on coupling of surface water and soil isotopes

Abstract

The present application belongs to the technical field of environmental monitoring and ecosystem assessment, and specifically discloses a characteristic component migration evolution prediction method based on shallow water and soil isotope coupling, which comprises the following steps: obtaining time series synchronous monitoring data of a target region, calculating a modified hydraulic gradient representing hydrological power, and obtaining macro-ecological parameters in space-time alignment; diagnosing the migration relay path of the characteristic component dynamically according to the above; based on the migration relay path and the concentration data of the characteristic component, a closed-loop feedback prediction model containing a negative feedback regulation mechanism is constructed; receiving a future hydrological event sequence, simulating and generating a space-time evolution scenario of the characteristic component by using the closed-loop feedback prediction model; and combining an ecological response evaluation benchmark list, the ecological environmental impact quantitative atlas of the target region is dynamically calculated and output. The present application couples the physical and chemical migration and biological interception mechanism, realizes the simulation of the space-time evolution scenario of the characteristic component and the dynamic quantitative assessment of the degree of ecological environmental impact.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring and ecosystem assessment technology, and relates to a method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling. Background Technology

[0002] In the fields of environmental science, agricultural resource management, and ecological restoration, accurately understanding the migration and transformation patterns of specific chemical components in shallow soil and water systems is fundamental to achieving precise governance. These "characteristic components" include both economically valuable beneficial nutrients (such as selenium (Se) and germanium (Ge) in naturally selenium-rich soil) and heavy metal pollutants with high ecological risks (such as cadmium (Cd) and lead (Pb). Currently, investigations into the migration and evolution of these components mainly rely on traditional physical hydrological models (such as HYDRUS-2D / 3D, MODFLOW, or MT3DMS), which use numerical simulations based on Darcy's law-based hydrodynamic equations and convection-dispersion equations.

[0003] However, existing technologies have the following significant limitations when applied to complex shallow layers:

[0004] First, there is distortion in the description of the physical dynamic field. Shallow water and soil media are heterogeneous and have multi-scale porosity, commonly exhibiting "preferred flow" and "dead water zones." Traditional hydrological models calculate hydrodynamics solely based on water level differences, failing to identify the true mixing ratio and connectivity pathways of water within microscopic pores, leading to significant discrepancies between simulated and measured solute transport velocities. Although hydrogen and oxygen stable isotope tracing techniques have been used in water cycle research, current technologies largely limit their application to water source identification, failing to dynamically couple their tracing parameters into dynamic correction models of chemical components.

[0005] Secondly, the biointerception mechanism is static and fragmented. The surface layer is the most active area for vegetation roots and microbial communities. Existing technologies often simplify biological processes to a static partition coefficient or adsorption term, neglecting the dynamic reduction effects of active plant uptake, microbial reduction and transformation, and biomass evolution on component fluxes. Especially in high-biomass areas such as farmland and wetlands, purely physicochemical models often overestimate component migration fluxes due to the lack of biointerception indicators.

[0006] Third, the assessment system suffers from lag and lacks a closed-loop mechanism. Existing ecological and environmental assessments mainly rely on annual statistical data or low-frequency remote sensing images, which is a post-hoc, static accounting model. It cannot respond in real time to the impact of short-term hydrological events such as torrential rain erosion and sudden seepage on the regional ecological carrying capacity. More importantly, existing technologies lack a closed-loop regulation mechanism and cannot simulate the chain reaction of accelerated effects after pollution stress leads to the failure of biological barrier. Summary of the Invention

[0007] To overcome the above-mentioned deficiencies of the prior art and to achieve the above objectives, the present invention proposes the following technical solution: a method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling, comprising: S1, acquiring a time-series synchronous monitoring dataset of multiple spatial points within the target area, wherein the time-series synchronous monitoring dataset includes hydrogen and oxygen stable isotope data, water level data, environmental factor data, and concentration data of characteristic components, wherein the characteristic components include beneficial nutrients or polluting heavy metal elements.

[0008] S2. Based on the stable isotope data of hydrogen and oxygen and the water level data, the corrected hydraulic gradient characterizing the hydrodynamics is calculated.

[0009] S3. Obtain biodiversity indices and vegetation physiological stress indices that are spatiotemporally aligned with the time-series synchronous monitoring dataset to form macro-ecological parameters.

[0010] S4. Based on the corrected hydraulic gradient, environmental factor data and macro-ecological parameters, the effective migration coefficient is calculated, and then the migration relay path of the characteristic components is dynamically diagnosed.

[0011] S5. Based on the migration relay path and concentration data of characteristic components, construct and run a closed-loop feedback prediction model that includes a negative feedback regulation mechanism.

[0012] S6. Receive an external event sequence containing rainfall and evaporation data that defines future hydrological events, and use a closed-loop feedback prediction model to simulate and generate spatiotemporal evolution scenarios of characteristic components.

[0013] S7. Call the preset list of ecological response evaluation benchmarks that defines the correspondence between ecological functions and environmental indicators. Based on the spatiotemporal evolution scenario and the list of ecological response evaluation benchmarks, dynamically calculate and output the quantitative map of the ecological and environmental impact of the target area.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention introduces hydrogen and oxygen stable isotope tracer correction coefficients to perform heterogeneity calibration on physical hydraulic gradients, identify preferential flow and dead water zone in water transport process, reduce the deviation generated by calculating dynamic field by relying solely on water level difference, and realize the quantitative simulation of real transport process of heterogeneous media.

[0015] (2) This invention transforms the microbial diversity index and vegetation physiological stress index into dynamic biological interception coefficients and integrates them into migration path diagnosis, which reduces the prediction bias of traditional models in vegetation-covered areas and ecologically active zones. It can quantitatively calculate the absorption, fixation and transformation flux of plant roots and microbial communities on characteristic components, reflecting the material cycling law under multi-sphere interaction.

[0016] (3) This invention establishes the correlation between the concentration changes of characteristic components and ecosystem trait indicators through a negative feedback regulation mechanism, realizes the feedback of the influence of concentration dynamics on migration rate, and can simulate the spatiotemporal evolution process of characteristic components driven by different hydrological events, providing a dynamic quantitative basis for ecological environment intervention and evaluation in the target area.

[0017] (4) By configuring characteristic component attribute identifiers and an ecological response evaluation benchmark list, this invention can be used to identify areas rich in beneficial nutrients and assess the diffusion risk of polluting heavy metal elements. By outputting a quantitative map of the ecological and environmental impact of the target area, the spatiotemporal dynamic accounting and spatial distribution analysis of the degree of ecological and environmental impact are realized. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 As shown, the method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling proposed in this invention includes: S1, acquiring a time-series synchronous monitoring dataset of multiple spatial points within the target area, wherein the time-series synchronous monitoring dataset includes hydrogen and oxygen stable isotope data, water level data, environmental factor data, and concentration data of characteristic components, including beneficial nutrients or polluting heavy metal elements.

[0022] Specifically, the engineering implementation process of acquiring the time-series synchronous monitoring dataset of multiple spatial points within the target area in this invention aims to construct a multi-dimensional, high-frequency, and spatiotemporally strictly aligned basic data base to eliminate the mismatch in sampling frequency and spatial distribution of multi-source heterogeneous monitoring data, and to provide a reliable underlying input for subsequent multi-physics coupling and migration path diagnosis.

[0023] First, a three-dimensional monitoring network covering the interaction nodes of surface water and shallow groundwater is constructed in the target area. Based on the topography and hydrogeological conditions, the system deploys a node array containing surface water monitoring sections and groundwater monitoring wells at multiple depths in the target area (such as catchment slopes, riverbank buffer zones, farmland, etc.), and uses real-time dynamic differential positioning technology to obtain the three-dimensional spatial coordinates of each spatial point.

[0024] Next, the system performs parallel acquisition of high-frequency and low-frequency data. For high-frequency data, the system uses in-situ sensors (such as submersible water level gauges and multi-parameter water quality probes) deployed at various spatial points in the three-dimensional monitoring network to continuously acquire high-frequency water level data and environmental factor data (such as redox potential, pH value, and water temperature) at sampling frequencies on the order of minutes or hours for the corresponding time series. For low-frequency data, since isotopes and specific chemical substances are difficult to detect in situ in real time, the system uses deployed automatic samplers to conduct intensive sampling before and after key hydrological events such as rainfall, or conducts regular manual sampling at fixed points. The water and soil samples are then sent to the laboratory for mass spectrometry and spectral / chromatographic analysis to obtain hydrogen and oxygen stable isotope data (including deuterium isotope ratios) at the corresponding discrete time points for the spatial points. Oxygen-18 isotope ratio Concentration data of characteristic components.

[0025] Finally, the system performs timestamp alignment and time-series interpolation assimilation processing based on a preset unified time step (e.g., a time step of 1 hour). Its engineering purpose is to bridge the temporal resolution gap between high-frequency sensor data and low-frequency laboratory data. The system extracts the time series of each spatial point. For high-frequency water level and environmental factor data, it uses a time window moving average method for downsampling to align them to a unified time step. For low-frequency hydrogen and oxygen stable isotope data and concentration data of characteristic components, the system uses cubic spline interpolation or a dynamic weighted interpolation algorithm based on the rate of change of adjacent high-frequency water levels for upsampling. The interpolation assimilation formula can be expressed as: ,in Align the target time point The isotope or concentration estimate, in units of U. , and These are adjacent discrete time points measured in the laboratory. The number representing the time interval. The i-th set of parameters representing the spline polynomial coefficients are obtained by calculating the spline polynomial coefficients based on measured discrete numerical values. This is the coefficient of the constant term, whose value is equal to the measured value at the starting point, and the unit is... For example, set it to 5.2; The coefficient of the linear term represents the rate of change at the nodes, in units of... For example, set it to 0.15; The coefficients of the quadratic term represent the curvature-related terms at the nodes, with units of . For example, set it to -0.02; The coefficient of the cubic term represents the rate of change of curvature, with units of... For example, it can be set to 0.005. After the above assimilation process, the system integrates the four types of data (water level, environmental factors, isotopes, and concentrations) with different frequencies into a multidimensional data tensor with a unified timestamp and spatial coordinates, generates the final time-series synchronous monitoring dataset, and outputs it to subsequent steps to drive the coupled calculation of hydrodynamics and chemical migration.

[0026] In this step, the parameters in the interpolation assimilation formula (such as...) The parameters (etc.) are not fixed constants, but are obtained by least squares fitting and calibration based on historical baseline survey data of the target area for no less than 12 months in the early stage, so as to ensure that the basic parameters of the model are highly matched with the local geological and hydrological characteristics.

[0027] S2. Based on the stable isotope data of hydrogen and oxygen and the water level data, the corrected hydraulic gradient characterizing the hydrodynamics is calculated.

[0028] In a preferred embodiment, the corrected hydraulic gradient characterizing hydrodynamics is calculated based on hydrogen and oxygen stable isotope data and water level data, including: acquiring the spatial coordinates of each spatial point in the time-series synchronous monitoring dataset, and calculating the physical hydraulic gradient between adjacent spatial points in combination with water level data.

[0029] Extract stable hydrogen and oxygen isotope data from each spatial point, calculate the isotope abundance difference between adjacent spatial points, and generate isotope tracer correction coefficients that reflect the true mixing and transport connectivity of water.

[0030] By coupling the physical hydraulic gradient with the isotope tracer correction coefficient, a corrected hydraulic gradient characterizing hydrodynamics is obtained.

[0031] Specifically, the engineering implementation process of calculating and correcting the hydraulic gradient based on hydrogen and oxygen stable isotope data and water level data in this invention aims to overcome the defects of traditional pure physical hydrological models in shallow complex porous media (such as those with preferential flow or dead water zones) by combining macroscopic gravitational potential energy with microscopic molecular tracing to quantify the real hydrological dynamic field that drives the cross-interface migration of characteristic components.

[0032] First, based on the spatial coordinates of each spatial point in the time-series synchronous monitoring dataset, and combined with the elevation and real-time water level data of each point, the physical hydraulic gradient between adjacent spatial points is calculated. The engineering objective is to obtain the theoretical potential energy of water flow driven by macroscopic gravity. The system calculates the physical hydraulic gradient between spatial point A and its adjacent spatial point B using the following formula: ,in, The physical hydraulic gradient; and The absolute water level elevations for points A and B are obtained based on the water level and elevation data in the time-series synchronous monitoring dataset. Let be the Euclidean distance between the two points.

[0033] At the same time, the system extracts the stable isotope data of hydrogen and oxygen corresponding to each spatial point (including the deuterium isotope ratio). Oxygen-18 isotope ratio The system calculates the isotopic abundance differences between adjacent spatial points and generates isotopic tracer correction coefficients accordingly. Its engineering purpose is to utilize the natural tracer properties of isotopes to identify the true source and mixing ratio of water, thereby correcting for heterogeneous transport characteristics not reflected in the physical hydraulic gradient. The system first calculates the isotopic Euclidean distance between spatial point A and its adjacent spatial point B to characterize the abundance difference: ,in, These are the deuterium isotope ratios corresponding to spatial point A and its adjacent spatial point B, respectively. These represent the oxygen-18 isotope ratios corresponding to spatial point A and its adjacent spatial point B, respectively. Subsequently, the system substitutes the abundance difference into a preset connectivity mapping function to generate isotope tracer correction coefficients, the calculation formula of which is as follows: ,in, is the isotope tracing correction coefficient, whose value range is between (0,1]; e is the natural constant; An empirical constant reflecting the dispersion characteristics of local hydrogeology. When the isotopic characteristics of two points are highly consistent ( (approaching 0) A value close to 1 indicates a strong and direct hydraulic connection and actual water transport between the two points; conversely, a value close to 1 indicates the presence of stagnant water or obstruction of mixing between different water sources.

[0034] The physical mechanism for calculating isotope tracer correction coefficients lies in: utilizing deuterium (D) and oxygen-18 ( 18O) Isotopic fractionation characteristics during water evaporation and mixing can be used to identify whether there is actual particle exchange between adjacent monitoring points. When the physical hydraulic gradient is large but the isotopic abundance difference is extremely small, it indicates the existence of a "preferred flow" channel; conversely, if the isotopic difference is extremely large, it suggests the existence of a hydraulic barrier or a "dead zone" between the two points. By correcting the physical gradient with an isotopic tracer correction coefficient, the actual hydrodynamic intensity driving the migration of characteristic components can be quantified.

[0035] Finally, the system couples the physical hydraulic gradient with the isotope tracer correction coefficient to obtain the corrected hydraulic gradient characterizing the hydrodynamics. Its engineering purpose is to provide an initial driving signal that combines physical drive and real connectivity for subsequent migration relay path diagnosis. The coupling calculation formula is as follows: .in, This refers to the corrected hydraulic gradient from point A to point B. The system traverses all adjacent spatial point pairs within the target region, generates the corrected hydraulic gradient spatial vector field of the target region under the current time slice, and outputs it to the next step to determine the first interface migration of characteristic components.

[0036] In this invention, "characteristic components" refer to specific chemical elements or compounds that have ecological sensitivity or economic development value in the target area, including but not limited to: beneficial nutrient elements (such as selenium (Se), germanium (Ge), and zinc (Zn)) and typical polluting heavy metal elements (such as cadmium (Cd), lead (Pb), and arsenic (As). "Migration relay path" refers to the discrete topological path sequence of characteristic components transferred across media between "surface water-pore water-soil particles-biointerface".

[0037] S3. Obtain biodiversity indices and vegetation physiological stress indices that are spatiotemporally aligned with the time-series synchronous monitoring dataset to form macro-ecological parameters.

[0038] In a preferred embodiment, a biodiversity index and a vegetation physiological stress index that are spatiotemporally aligned with a time-series synchronous monitoring dataset are acquired to form macro-ecological parameters, including: acquiring environmental molecular biomarker data and performing bioinformatics analysis to generate a biodiversity index that reflects the structure of microbial and aquatic biological communities.

[0039] Acquire multispectral UAV remote sensing images and perform spectral analysis on the multispectral UAV remote sensing images to generate a vegetation physiological stress index that reflects the vegetation growth status and physiological stress status.

[0040] By combining the biodiversity index with the vegetation physiological stress index, macro-ecological parameters are generated.

[0041] Specifically, the engineering implementation process for obtaining macroscopic ecological parameters in this invention provides quantifiable biological response feedback signals for subsequent migration relay path diagnosis. This process first performs bioinformatics analysis of environmental molecular biomarker data to generate a biodiversity index. Its engineering objective is to transform the information of mixed biological genetic material in field-collected soil and water samples into numerical indicators characterizing the stability and complexity of the microbial and aquatic biological community structure in a specific area. In this step, the system receives environmental molecular biomarker data that is strictly aligned with the time-series synchronous monitoring dataset in terms of sampling time and spatial coordinates. This data is typically the raw sequence file output by a high-throughput sequencer.

[0042] First, a bioinformatics processing workflow is executed, including quality control of the original sequences, removal of adapters and low-quality reads. Then, similar sequences are aggregated using a taxonomic clustering algorithm and compared with public gene databases to complete species classification annotation. Finally, a biodiversity index is calculated based on the relative abundance of each species' taxonomic unit, using the following formula: ,in, For biodiversity index; This represents the proportion of sequences from the j-th taxonomic unit to the total number of valid sequences in the sample. This proportion is obtained directly from the abundance statistics table after species annotation, where j is the taxonomic unit number. , This represents the total number of species taxa detected in the sample.

[0043] Next, the system processes spatiotemporally aligned multispectral UAV remote sensing images in parallel to generate a vegetation physiological stress index. Its engineering objective is to rapidly and extensively assess the health status of vegetation in a target area, particularly its sensitivity to physiological stresses caused by abnormal concentrations of specific components, using non-contact remote sensing. The system acquires remote sensing images taken by a UAV multispectral camera in specific bands such as near-infrared and red-edge, and performs radiometric calibration and geometric correction to eliminate the influence of lighting conditions and flight attitude, obtaining a true surface reflectance image. Based on the corrected reflectance data, the system calculates the vegetation physiological stress index, using the following formula: ,in, The vegetation physiological stress index; The surface reflectance is in the near-infrared band, and this data is extracted from the corresponding pixels in the near-infrared band image. The red-edge band represents the surface reflectance, extracted from the corresponding pixels in the red-edge band image. The vegetation physiological stress index is sensitive to changes in chlorophyll concentration and can effectively reflect early physiological changes in vegetation caused by environmental stress.

[0044] Finally, the system spatiotemporally fuses the biodiversity indices generated in the first two steps with the vegetation physiological stress indices to jointly construct macro-ecological parameters. Its engineering objective is to construct a multi-dimensional biological state vector, providing comprehensive data support for subsequent calculations of biological interception effects. The system matches the point-distributed biodiversity indices with the areal-distributed vegetation physiological stress indices on a unified geographic grid through spatial interpolation or association with nearest-neighbor pixels, forming a comprehensive dataset containing biodiversity and vegetation health status for each grid cell; this dataset constitutes the macro-ecological parameters.

[0045] In this invention, the scientific basis for selecting the biodiversity index and the vegetation physiological stress index lies in the fact that microorganisms are microscopic "converters," influencing the bioavailability of components through metabolism (such as microbial reduction of selenium); vegetation is macroscopic "interceptors," reducing migration fluxes through root adsorption and physical barriers. The coupling of the two enables cross-scale evaluation from the molecular scale to the landscape scale.

[0046] S4. Based on the corrected hydraulic gradient, environmental factor data and macro-ecological parameters, the effective migration coefficient is calculated, and then the migration relay path of the characteristic components is dynamically diagnosed.

[0047] In a preferred embodiment, an effective migration coefficient is calculated based on the modified hydraulic gradient, environmental factor data, and macro-ecological parameters, and then the migration relay path of the characteristic components is dynamically diagnosed, including: using the modified hydraulic gradient as the initial driving signal to determine the first target microenvironment in which the characteristic components undergo their first interfacial migration.

[0048] Real-time environmental factor data corresponding to the first target microenvironment are obtained, and physicochemical migration coefficients characterizing the chemical retention effect are generated accordingly.

[0049] The macro-ecological parameters corresponding to the microenvironment of the first target are analyzed, and a biological interception coefficient characterizing the biological fixation effect is generated accordingly.

[0050] The effective migration coefficient is obtained by coupling the physicochemical migration coefficient with the biological interception coefficient.

[0051] Using the effective migration coefficient as state feedback and combining it with the corrected hydraulic gradient of the next time slice, the subsequent interface migration is iteratively judged until a complete migration relay path is generated.

[0052] Specifically, the engineering implementation process of dynamically diagnosing the migration relay path of characteristic components in this invention aims to accurately simulate the actual movement trajectory and flux decay of characteristic components in complex ecological environments based on multi-physics coupling. This process first uses the modified hydraulic gradient at the current moment as the initial driving signal to determine the first target microenvironment where the characteristic component undergoes its first interfacial migration. By analyzing the spatial potential energy field formed by the modified hydraulic gradient, the system automatically identifies and locks onto the adjacent spatial grid cell with the fastest decrease in potential energy gradient. This cell is then determined as the first target microenvironment, thus completing the initial determination of the migration direction.

[0053] After identifying the primary target microenvironment, the system immediately acquires real-time environmental factor data corresponding to that microenvironment and generates physicochemical migration coefficients accordingly. Its engineering purpose is to quantify the stability and migration capacity of characteristic components under the chemical conditions of this microenvironment. The system invokes an internal geochemical model, which includes a mapping table between environmental factors and fixation rates. For example, when a specific heavy metal is at pH > 7 and Eh > 0, it is determined that precipitation is the primary occurrence, and the output is... The value is 0.8; the output is under a slightly acidic reducing environment. The value is 0.1, etc. Using acquired environmental factor data (such as redox potential Eh ranging from -200mV to +300mV, pH value ranging from 5.5 to 8.0) as input, the proportion of chemical precipitation or speciation transformation of characteristic components under this environment is calculated. The formula for calculating the physicochemical migration coefficient is... .in, The physicochemical migration coefficient is a dimensionless value. The chemical fixation rate of the characteristic components is output by the geochemical model based on environmental factor data, and its value is in the range [0,1]. This parameter reflects the proportion of components that are transformed into the non-mobile phase due to precipitation, complexation, or ion exchange.

[0054] Simultaneously, the system analyzes the macroscopic ecological parameters corresponding to the first target microenvironment in parallel to generate a biological interception coefficient. Its engineering purpose is to quantify the absorption and fixation effects of plant and microbial communities on the migrating characteristic components. The system calculates the biological interception coefficient based on the biodiversity index and vegetation physiological stress index from the macroscopic ecological parameters. The formula for calculating the biological interception coefficient is as follows: ,in, This represents the biological interception coefficient. and The preset weighting coefficients represent the respective contributions of biological communities and vegetation to the interception effect. Their values ​​are in the range of [0,1]. For example, the contribution weight of biological communities is set to 0.8 in groundwater environments or 0.4 in surface water bodies, and the contribution weight of vegetation interception is set to 0.6 in vegetation-covered areas. and These are the bioabsorber coefficient and the vegetation fixer coefficient, respectively.

[0055] Subsequently, the system performs coupled calculations based on the physicochemical migration coefficient and the biological interception coefficient to generate the diagnostic results for the first path segment. Its engineering objective is to integrate the dual effects of physicochemical and biological processes to determine the final effective migration capability of this migration step. The formula for generating the effective migration coefficient through coupled calculations is as follows: ,in, The effective migration coefficient represents the proportion of a characteristic component that can continue to migrate after undergoing both chemical and biological interception. The diagnostic result for the first pathway segment is a vector containing a direction vector from the starting point to the first target microenvironment and a numerical value. A comprehensive data package.

[0056] Finally, the system uses the diagnostic results of the first path segment as status feedback and, combined with the corrected hydraulic gradient calculated in the next time slice, initiates the iterative calculation process. The system takes the first target microenvironment as the new migration starting point, and the initial amount of the migratable characteristic components is the input amount of the previous stage multiplied by the effective migration coefficient. Then, the above steps are repeated to determine the next target microenvironment and calculate its effective migration coefficient. This iterative process continues until the effective migration coefficient is lower than the preset cutoff threshold or the simulation time ends, ultimately generating a complete migration relay path composed of multiple path segments connected sequentially.

[0057] In a further preferred embodiment, the macro-ecological parameters corresponding to the first target microenvironment are analyzed, and a biological interception coefficient characterizing the biological fixation effect is generated accordingly, including: extracting the biodiversity index and vegetation physiological stress index corresponding to the first target microenvironment from the macro-ecological parameters.

[0058] Obtain a database of species functional traits that defines the interaction characteristics between biological groups and characteristic components;

[0059] Based on the biodiversity index and by querying the species functional trait database, specific functional biological communities and their abundance are identified, and biological absorber coefficients are generated.

[0060] Based on the vegetation physiological stress index, the enrichment capacity of vegetation communities for characteristic components is assessed, and vegetation fixation coefficients are generated.

[0061] The bio-interception coefficient is obtained by weighting and combining the bio-absorber coefficient and the vegetation fixation coefficient.

[0062] Specifically, the engineering implementation process of parsing macroscopic ecological parameters and generating biological interception coefficients in this invention aims to transform biological indicators extracted from environmental molecular biomarker data and multispectral UAV remote sensing imagery into quantifiable biological action parameters that can be directly coupled with physicochemical models. This process first extracts the biodiversity index and vegetation physiological stress index corresponding to the spatial location of the current target microenvironment from the macroscopic ecological parameters. Based on the geographic coordinates of the target microenvironment, the system retrieves and locks the corresponding index values ​​in the macroscopic ecological parameter dataset.

[0063] After obtaining these two key indices, the system generates the bioabsorbent coefficient. Its engineering purpose is to quantify the absorption or transformation capacity of microbial and aquatic communities in the soil and water environment for specific components. The system uses the acquired biodiversity indices as input and queries a pre-defined species functional trait database, which records the metabolic, enrichment, or transformation characteristics of different biological groups for specific components. Based on the species composition reflected by the biodiversity indices, the system determines whether there are biological groups with specific adsorption or transformation functions for the components and their activity levels, and calculates the bioabsorbent coefficient accordingly. The calculation formula is as follows: ,in, The bioabsorbent coefficient; For the first The uptake rate per unit abundance of characteristic components by a functional biological group. This parameter was obtained from a species functional trait database, and its value is in the range of (0,1]. For example, a value of 0.15 can be set. The number representing a biological group with specific functions. ; For the first The relative abundance of functional biological groups, this data can be obtained from the biodiversity index. The intermediate results are obtained during the calculation process; This is a function used to describe the relationship between abundance and absorption efficiency, defined based on a microbial kinetic model, with values ​​ranging from (0,1). For example, the Michaelis-Menten equation can be used to characterize the absorption saturation effect at high abundance. To meet the objective upper limit of physical retention, the cumulative value calculated by the above formula... When it is greater than 1, The final output value is truncated to 1.

[0064] Simultaneously, the system generates vegetation fixation coefficients. Its engineering purpose is to quantify the absorption and fixation capacity of vegetation roots or aboveground parts for specific components, particularly assessing the contribution of vegetation to this process under different health conditions. The system will acquire vegetation physiological stress indices. As input, calculate the vegetation fixation coefficient. Its calculation formula is ,in, It is a predefined response function, such as an inverted U-shaped quadratic polynomial function, used to describe the nonlinear effect of vegetation health on its absorption capacity. For example, when... This indicates that under moderate stress, the absorption of certain heavy metals by some plants may actually be enhanced. The vegetation cover of the region can be directly calculated from the visible light band of UAV remote sensing images.

[0065] Finally, the system integrates the bioabsorption sub-coefficient and the vegetation fixation sub-coefficient to calculate the final biointerception coefficient. Its engineering purpose is to integrate the interception effects from different biospheres (microorganisms, plants) into a unified parameter. Since both sub-coefficients represent the proportion of characteristic components fixed by organisms, the system integrates them through a weighted summation to ultimately calculate the biointerception coefficient. This biointerception coefficient is then output to correct the migration flux in the physicochemical model.

[0066] S5. Based on the migration relay path and concentration data of characteristic components, construct and run a closed-loop feedback prediction model that includes a negative feedback regulation mechanism.

[0067] S6. Receive an external event sequence containing rainfall and evaporation data that defines future hydrological events, and use a closed-loop feedback prediction model to simulate and generate spatiotemporal evolution scenarios of characteristic components.

[0068] In a preferred embodiment, based on the migration relay path and the concentration data of characteristic components, a closed-loop feedback prediction model including a negative feedback adjustment mechanism is constructed and run, including: constructing a migration and transport network of characteristic components according to the topology of the migration relay path and the effective migration coefficient of each path segment.

[0069] The concentration data of the characteristic components are used as the initial state values ​​of the migration and transport network;

[0070] A negative feedback regulation mechanism is integrated into the migration and transport network. This mechanism is configured to dynamically update macro-ecological parameters based on simulated changes in the concentration of characteristic components, and thereby adjust the biological interception coefficient in the migration and transport network.

[0071] By integrating the migration transmission network, initial state values, and negative feedback adjustment mechanisms, a closed-loop feedback prediction model is formed.

[0072] Specifically, the engineering implementation process of constructing and running the closed-loop feedback prediction model in this invention aims to establish a computational engine capable of simulating the dynamic evolution of characteristic components in a real ecological environment driven by future hydrological events, and to introduce a self-correction mechanism to improve the fidelity of long-term predictions. This process first constructs a migration and transport network of characteristic components in a multi-media environment based on the migration direction and effective migration coefficient of each path segment in the migration relay path. The system discretizes the target area into a series of spatial grid nodes, resolves the migration relay path into directed connections between nodes, and assigns the effective migration coefficient corresponding to each path segment to the connection weight, thereby constructing a mathematical graph network model, which is the migration and transport network.

[0073] After the migration and transport network is constructed, the system uses the concentration data of characteristic components as the initial state values ​​of the migration and transport network. Specifically, the system assigns the initial time-series characteristic component concentration values ​​corresponding to each spatial grid node obtained from the time-series synchronous monitoring dataset as the initial state attribute of that node, thereby completing the model initialization.

[0074] The core step involves introducing a negative feedback adjustment mechanism into the migration and transport network. Its engineering purpose is to simulate the ecosystem's response to environmental changes, enabling the model to dynamically adjust migration parameters based on the ecological changes caused by the migration process itself, rather than using statically unchanged parameters for prediction. The negative feedback adjustment mechanism is configured such that when the concentration change of a characteristic component in a microenvironment exceeds a preset threshold during simulation, for example, a concentration change exceeding 15% within 24 hours, the system will automatically trigger a dynamic update of the corresponding macro-ecological parameters for that microenvironment. The triggering condition is... ,in, The result of the trigger condition determination; Simulated concentration at the current moment; The simulated concentration at the previous moment; The preset concentration change threshold is determined through a preset dynamic calibration mechanism based on the ecological response characteristics and historical statistical patterns of the target microenvironment, for example, set to 15%. After triggering, the system will recalculate the biological interception coefficient and effective migration coefficient at that location based on the updated macro-ecological parameters. This dynamically adjusted effective migration coefficient will be used for migration simulation at subsequent time steps to correct the subsequent migration process.

[0075] Ultimately, the system integrates the migration transport network, initial state values, and negative feedback adjustment mechanism to form a complete closed-loop feedback prediction model. During runtime, the system uses externally defined future hydrological event sequences (such as hourly rainfall and evaporation data for the next 72 hours) as boundary condition inputs to the model. The model iteratively calculates in discrete time steps. Within each time step, it calculates the component fluxes between nodes based on the migration transport network and the current effective migration coefficients, updates the concentration state of each node, and determines whether to update the migration parameters based on the negative feedback adjustment mechanism, until the simulation of the entire event sequence is completed, outputting the final spatiotemporal evolution scenario of characteristic component concentrations.

[0076] In this invention, the core logic of the negative feedback regulation mechanism lies in simulating the adaptive and damage response process of the ecosystem: when the simulated concentration of pollutant components exceeds the biological tolerance threshold, the model will automatically reduce the biological interception coefficient to simulate the accelerated diffusion scenario of pollution after the failure of the interception barrier; conversely, in the nutrient enrichment scenario, the interception coefficient is increased to reflect the self-repair capacity of the ecosystem.

[0077] In a further preferred embodiment, a negative feedback regulation mechanism is integrated into the migration transmission network. This mechanism is configured to dynamically update macro-ecological parameters based on simulated changes in characteristic component concentrations, including: if the simulated increase in characteristic component concentration indicates pollution stress, then according to a preset ecotoxicological response relationship driven by the net increase in pollutant concentration and conforming to an exponential decay model, the biodiversity index and / or vegetation physiological stress index are downgraded.

[0078] If the increase in the concentration of the simulated characteristic components indicates nutrient enrichment, then the vegetation physiological stress index is increased according to the preset ecological gain response relationship driven by the net increase in the concentration of beneficial nutrients and conforming to a monotonically increasing response function.

[0079] The biological interception coefficient was recalculated based on the updated biodiversity index and / or vegetation physiological stress index.

[0080] Specifically, the dynamic updating process of macroscopic ecological parameters within the negative feedback regulation mechanism of this invention aims to transform the simulated chemical concentration changes in the closed-loop feedback prediction model into quantitative adjustments to biological state indicators, thereby achieving bidirectional coupling between chemical and biological processes. This process is triggered when the concentration change of a characteristic component in a certain microenvironment within the model exceeds a preset threshold.

[0081] Once triggered, the system first executes differentiated response logic based on the attribute definitions of the characteristic components. If the simulated increase in the concentration of the characteristic components indicates pollution stress, the system will downgrade the biodiversity index and / or vegetation physiological stress index according to a preset ecotoxicity response relationship. Its engineering purpose is to simulate the inhibitory effect of pollutant accumulation on biological communities and vegetation health. The downgrade process is executed through a dose-response function, and the update formula is as follows: as well as ,in, and These are the updated biodiversity index and vegetation physiological stress index; and These are the biodiversity index and vegetation physiological stress index before the update; It is the net increase in pollutant concentration, obtained by subtracting the simulated concentration from the current time and the previous time. and This is a pre-defined, monotonically decreasing toxicity response function, the specific form of which is calibrated using experimental data or literature, used to describe the rate of decline in biodiversity and vegetation health under different concentration increments. For example, in one specific embodiment, the toxicity response function adopts an exponential decay model, the specific expression of which is: ,in These are toxicological constants calibrated based on the target region, and their dimensions are the reciprocal of the concentration unit (e.g., L / mg or kg / mg). This represents the net increase in the concentration of pollutants.

[0082] Conversely, if the increased concentration of simulated characteristic components indicates nutrient enrichment, such as the accumulation of the beneficial nutrient selenium, the system will increase the vegetation physiological stress index according to a preset ecological gain-response relationship. Its engineering purpose is to simulate the promoting effect of beneficial nutrients on plant growth; typically, improved health is reflected in specific spectral indices. The increase process is also executed through a gain effect function, whose update formula is... ,in, It is the net increase in the concentration of beneficial nutrients, obtained by subtracting the simulated concentration from the current time and the previous time. It is a pre-defined monotonically increasing gain response function used to characterize the positive impact of nutrient enrichment on vegetation physiological conditions.

[0083] After updating the biodiversity index or vegetation physiological stress index, the system immediately recalculates the biological interception coefficient of the affected microenvironment based on these updated index values. The system invokes the aforementioned biological interception coefficient calculation steps, substituting the updated biodiversity index and / or vegetation physiological stress index back into the mapping relationship as input, thereby generating a new biological interception coefficient value that reflects the current ecological response state. This newly generated biological interception coefficient replaces the original coefficient and is used to calculate the migration flux at the next time step of the closed-loop feedback prediction model, enabling the entire model's predictive behavior to dynamically adapt to the environmental changes it simulates.

[0084] S7. Call the preset list of ecological response evaluation benchmarks that defines the correspondence between ecological functions and environmental indicators. Based on the spatiotemporal evolution scenario and the list of ecological response evaluation benchmarks, dynamically calculate and output the quantitative map of the ecological and environmental impact of the target area.

[0085] In a preferred embodiment, based on the spatiotemporal evolution scenario and the ecological response evaluation benchmark list, the ecological and environmental impact quantification map of the target area is dynamically calculated and output, including: performing spatial analysis on the spatiotemporal evolution scenario and extracting the net change in the concentration of characteristic components for each spatial grid.

[0086] Extract the land use type identifier and characteristic component attribute identifier for each spatial grid, use these as indexes to retrieve the corresponding response function in the ecological response evaluation benchmark list, substitute the net change in characteristic component concentration into the response function for function calculation, and output the ecological environment function quantity correction coefficient.

[0087] The baseline ecological and environmental function quantities of each spatial grid under the corresponding land use type are extracted from the basic geographic information database, and multiplied by the ecological and environmental function quantity correction coefficient to obtain the corrected ecological and environmental function quantity accounting parameters.

[0088] The system retrieves the corresponding unit environmental capacity parameters from the preset ecological and environmental carrying capacity parameter library. Combined with the corrected ecological and environmental function quantity accounting parameters, it calculates the ecological and environmental impact index of each spatial grid at each predicted time point. Then, it maps the index to the three-dimensional geographic information system according to spatial coordinates and timestamps to generate a spatial distribution layer of environmental impact that changes with time series, which serves as a quantitative map of ecological and environmental impact.

[0089] Specifically, the engineering implementation process of dynamically calculating and outputting a quantitative map of ecological and environmental impacts in this invention aims to transform the microscopic substance concentration changes output by the closed-loop feedback prediction model into a dynamic and forward-looking assessment of the macroscopic ecological and environmental carrying capacity. This process first performs spatial gridding analysis on the spatiotemporal evolution scenario to extract the net change in characteristic component concentration for each spatial grid within the prediction time range. The system receives this four-dimensional dataset of the spatiotemporal evolution scenario. For each spatial grid unit, it calculates the difference in characteristic component concentration between the end of the prediction period and the initial time; this difference is the net change in concentration. The concentrations of characteristic components at the predicted endpoint and initial time were directly extracted from the spatiotemporal evolution scenario data.

[0090] Next, the system matches the net concentration change of each grid with a pre-set ecological response assessment benchmark list to dynamically adjust the ecological and environmental function calculation parameters corresponding to each grid. The engineering objective is to establish a quantitative mapping relationship from changes in chemical indicators to changes in the physical quantity of ecosystem services. The ecological response assessment benchmark list is a structured database that stores the response functions between various ecological and environmental functions and key environmental indicators for different land use types, such as agricultural land, industrial construction land, or ecological forest land. Based on the land use type and characteristic component attributes of each grid, the system calculates the net concentration change... Substituting the corresponding response function, the correction coefficient is calculated, and this coefficient is used to adjust the baseline ecological environment function quantity of the grid. The calculation formula for the corrected ecological environment function quantity accounting parameters is as follows: ,in, These are the revised ecological and environmental function quantity accounting parameters; The baseline functional quantities for this grid, such as crop yield per unit area or water conservation capacity, are obtained from the basic geographic information database. For land use types identified from the ecological response assessment benchmark list The response functions of the characteristic components.

[0091] Finally, the system utilizes the corrected ecological and environmental function quantity accounting parameters and employs a preset ecological and environmental impact accounting method to calculate and integrate the data to generate a quantitative ecological and environmental impact map. Its engineering objective is to transform the corrected ecological and environmental physical quantities into environmental impact indices and visually present their dynamic evolution in the form of a spatiotemporal map. The system calls upon a library of ecological and environmental carrying capacity parameters containing various environmental impact weights, multiplying the ecological and environmental function quantity accounting parameters of each grid at each time point within the prediction period with the corresponding unit environmental capacity parameter to obtain the ecological and environmental impact degree index of that grid at that time point. The system calculates the indices for all grids at all prediction time points, and finally overlays these data onto a three-dimensional geographic information system to form a dynamically changing spatial distribution layer of environmental impact over time—this is the quantitative ecological and environmental impact map.

[0092] In a further preferred embodiment, the corrected ecological environment function quantity calculation parameters are obtained, including: if the characteristic component is a beneficial nutrient element, then the ecological environment function quantity calculation parameters related to the ecological quality of agricultural products are positively increased according to the ecological environment function quantity correction coefficient.

[0093] If the characteristic component is a polluting heavy metal element, then the ecological and environmental function quantity calculation parameters related to water purification and biodiversity maintenance will be negatively reduced based on the ecological and environmental function quantity correction coefficient.

[0094] Record the dynamic correction process of ecological environment function quantity accounting parameters and their corresponding spatiotemporal information to form a correction log.

[0095] Specifically, the engineering implementation process of dynamically correcting the accounting parameters of ecological and environmental functions in this invention aims to perform differentiated quantitative adjustments to the underlying physical parameters of ecological and environmental indicators based on the ecological attributes (beneficial or harmful) of characteristic components. This process is initiated through a conditional judgment logic, whereby the system first reads the characteristic component attribute identifiers of each spatial grid.

[0096] If the specialty component is a beneficial nutrient element, and its net concentration change is positive, the system will positively increase the ecological and environmental function parameters related to improving the ecological quality of agricultural products. The engineering objective is to quantify the enrichment effect of beneficial nutrients in soil or water bodies as an ecological evaluation bonus for specialty agricultural products. The system calls a preset gain response function, which is constructed based on agricultural experimental data and describes the nonlinear relationship between the increase in beneficial nutrient concentration and the improvement in the quality rate of agricultural products. The corrected parameter calculation formula is as follows: ,in, These are the revised parameters of ecological and environmental functions related to the ecological quality of agricultural products. These are the baseline functional quantity accounting parameters for grid-based agricultural products, such as the baseline output value under standard yield, obtained based on agricultural statistical data or basic geographic information databases. It is the net change in concentration; It is a dimensionless gain response function, constructed based on a preset agricultural experiment response curve. Its output value represents the percentage increase in value, and its value is in the range of [0,1]. For example, when the concentration increases by 2 mg / kg, the value is set to 0.2 (i.e., an increase of 20%).

[0097] Conversely, if the characteristic component is a polluting heavy metal element, and its net concentration change is positive, the system will negatively reduce parameters related to water purification, biodiversity maintenance, and other ecological and environmental functions. The engineering purpose is to quantify the damage to ecosystem services caused by pollutant diffusion. The system calls a preset degradation response function, which is constructed based on ecotoxicological data and describes the inhibitory effect of increased pollutant concentration on water purification capacity or biological community health. The corrected parameter calculation formula is as follows: ,in, These are the revised parameters of ecological and environmental functions related to environmental regulation; This is the baseline service volume of the function when it is uncontaminated, such as the baseline water cleanliness index, which is obtained based on the ecosystem service assessment model or the baseline environmental database. Its value is greater than or equal to 0, for example, the value is set to 10000. It is the net change in concentration; It is a dimensionless degradation response function, constructed based on a preset ecotoxicological response curve. Its output value represents the proportion of service function loss, and its value is in the range of [0,1]. For example, when the concentration increases by 5 mg / L, the value is set to 0.2 (i.e., a loss of 20%).

[0098] In this embodiment, the weighting coefficients in the response function of the ecological response evaluation benchmark list are calibrated based on the statistical correlation between crop yield and soil selenium content in the region over a five-year period, ensuring the authority and accuracy of the calculation parameters.

[0099] After completing any of the above correction calculations, the system will record the dynamic correction process of the ecological and environmental function parameters and their corresponding spatiotemporal coordinates. The engineering purpose of this step is to provide a complete audit trail for subsequent ecological restoration assessments and decision-making traceability. The system will generate a structured log containing a unique grid cell identifier, timestamp, parameter value before correction, net concentration change that triggered the correction, applied response function identifier, and parameter value after correction, and store it in the database.

[0100] In a further preferred embodiment, after dynamically calculating and outputting the ecological and environmental impact quantification map of the target area, the method further includes: performing spatiotemporal analysis on the ecological and environmental impact quantification map to identify high-risk areas and areas of ecological quality improvement that are expected to change in ecological and environmental impacts.

[0101] For high-risk areas, and in conjunction with migration relay paths, a set of risk intervention strategies is generated that includes engineering interception and emergency control measures;

[0102] For areas where ecological quality has improved, and in conjunction with current land use data, a set of ecological efficiency enhancement strategies is generated, including suggestions for ecological restoration layout and environmental capacity regulation.

[0103] Specifically, the engineering implementation process of the decision-making strategy based on the quantitative ecological and environmental impact map in this invention aims to transform complex environmental prediction results into actionable recommendations that directly guide management practices. This process first automatically identifies key areas of expected changes in ecological and environmental impact based on the quantitative ecological and environmental impact map. The system performs spatiotemporal change rate analysis on the quantitative ecological and environmental impact map data. By setting change rate thresholds (e.g., a decrease of more than 20% or an increase of more than 30% in ecological and environmental impact during the prediction period), it filters out high-risk areas with a significant expected decrease in ecological and environmental impact and areas with a significant expected increase in ecological quality improvement. The system outputs the spatial extent, magnitude of quality changes, and trends of these areas as structured data.

[0104] After identifying high-risk areas, the system generates a set of risk intervention strategies for these areas, including engineering interception and emergency control measures. Its engineering purpose is to provide managers with a set of alternative solutions to avoid or mitigate expected ecological losses. The system performs geospatial overlay analysis on the spatial information of high-risk areas with a pre-set database of engineering facilities (such as the location and capacity parameters of pumping stations, gates, and emergency treatment facilities), and automatically matches and optimizes intervention measures that can effectively block pollution paths, combined with previously generated migration relay paths. For example, the system may generate instructions to activate a pumping station with a specific power at a specific coordinate upstream of the migration relay path or to close a gate at a river mouth. These strategies are combined into a risk intervention strategy set that includes the type of measure, implementation location, activation time, and operational parameters.

[0105] Simultaneously, after identifying areas for ecological quality improvement, the system will generate a set of ecological efficiency enhancement strategies for these areas, including suggestions for land use optimization and ecological restoration layout. Its engineering purpose is to help decision-makers seize opportunities for ecological resource appreciation and achieve sustainable development. The system compares and analyzes the spatial scope of areas for ecological quality improvement with existing land use planning maps to identify areas where current use patterns are mismatched with potential ecological carrying capacity. For example, if a piece of ordinary farmland is predicted to be a highly enriched area of ​​beneficial nutrients, the system will mark it and retrieve a suggestion from the strategy library to "adjust it to a specialty ecological agricultural product planting area," along with a relevant environmental quality assessment summary. These suggestions are integrated into a set of ecological efficiency enhancement strategies that includes regional identification, ecological enhancement potential assessment, and suggested adjustment directions.

[0106] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for predicting the migration and evolution of characteristic components based on shallow soil-water isotope coupling, characterized in that, include: S1. Obtain a time-series synchronous monitoring dataset of multiple spatial points within the target area. The time-series synchronous monitoring dataset includes hydrogen and oxygen stable isotope data, water level data, environmental factor data, and concentration data of characteristic components, including beneficial nutrients or polluting heavy metal elements. S2. Based on the stable isotope data of hydrogen and oxygen and the water level data, the corrected hydraulic gradient characterizing the hydrodynamics is calculated. S3. Obtain biodiversity indices and vegetation physiological stress indices that are spatiotemporally aligned with the time-series synchronous monitoring dataset to form macro-ecological parameters; S4. Based on the corrected hydraulic gradient, environmental factor data and macro-ecological parameters, calculate the effective migration coefficient, and then dynamically diagnose the migration relay path of the characteristic components. S5. Based on the migration relay path and concentration data of characteristic components, construct and run a closed-loop feedback prediction model that includes a negative feedback regulation mechanism. S6. Receive an external event sequence containing rainfall and evaporation data that defines future hydrological events, and use a closed-loop feedback prediction model to simulate and generate spatiotemporal evolution scenarios of characteristic components. S7. Call the preset list of ecological response evaluation benchmarks that defines the correspondence between ecological functions and environmental indicators. Based on the spatiotemporal evolution scenario and the list of ecological response evaluation benchmarks, dynamically calculate and output the ecological and environmental impact quantification map of the target area.

2. The method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling according to claim 1, characterized in that, Based on hydrogen and oxygen stable isotope data and water level data, the corrected hydraulic gradient characterizing hydrodynamics is calculated, including: obtaining the spatial coordinates of each spatial point in the time-series synchronous monitoring dataset, and calculating the physical hydraulic gradient between adjacent spatial points in combination with water level data; Extract stable hydrogen and oxygen isotope data from each spatial point, calculate the isotope abundance difference between adjacent spatial points, and generate isotope tracer correction coefficients that reflect the true mixing and transport connectivity of water. By coupling the physical hydraulic gradient with the isotope tracer correction coefficient, a corrected hydraulic gradient characterizing hydrodynamics is obtained.

3. The method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling according to claim 1, characterized in that, Biodiversity indices and vegetation physiological stress indices, spatiotemporally aligned with the time-series synchronized monitoring dataset, are obtained to form macro-ecological parameters, including: Acquire environmental molecular biomarker data and perform bioinformatics analysis to generate a biodiversity index that reflects the structure of microbial and aquatic biological communities; Acquire multispectral UAV remote sensing images and perform spectral analysis on the multispectral UAV remote sensing images to generate a vegetation physiological stress index that reflects the vegetation growth status and physiological stress status. By combining the biodiversity index with the vegetation physiological stress index, macro-ecological parameters are generated.

4. The method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling according to claim 3, characterized in that, Based on the corrected hydraulic gradient, environmental factor data, and macro-ecological parameters, the effective migration coefficient is calculated, and then the migration relay paths of characteristic components are dynamically diagnosed, including: Using the modified hydraulic gradient as the initial driving signal, the first target microenvironment in which the characteristic components undergo their first interfacial migration is determined. Real-time environmental factor data corresponding to the first target microenvironment are obtained, and physicochemical migration coefficients characterizing the chemical retention effect are generated accordingly. The macro-ecological parameters corresponding to the microenvironment of the first target are analyzed, and a biological interception coefficient characterizing the biological fixation effect is generated accordingly. The effective migration coefficient is obtained by coupling the physicochemical migration coefficient with the biological interception coefficient. Using the effective migration coefficient as state feedback and combining it with the corrected hydraulic gradient of the next time slice, the subsequent interface migration is iteratively judged until a complete migration relay path is generated.

5. The method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling according to claim 4, characterized in that, The macroscopic ecological parameters corresponding to the microenvironment of the first target are analyzed, and based on these parameters, a biological interception coefficient characterizing the biological fixation effect is generated, including: Extract the biodiversity index and vegetation physiological stress index corresponding to the first target microenvironment from macro-ecological parameters; Obtain a database of species functional traits that defines the interaction characteristics between biological groups and characteristic components; Based on the biodiversity index and by querying the species functional trait database, specific functional biological communities and their abundance are identified, and biological absorber coefficients are generated. Based on the vegetation physiological stress index, the enrichment capacity of vegetation communities for characteristic components is assessed, and vegetation fixation coefficients are generated. The bio-interception coefficient is obtained by weighting and combining the bio-absorber coefficient and the vegetation fixation coefficient.

6. The method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling according to claim 1, characterized in that, Based on migration relay paths and concentration data of characteristic components, a closed-loop feedback prediction model incorporating a negative feedback regulation mechanism was constructed and run, including: Based on the topology of the migration relay path and the effective migration coefficient of each path segment, a migration transmission network for characteristic components is constructed. The concentration data of the characteristic components are used as the initial state values ​​of the migration and transport network; A negative feedback regulation mechanism is integrated into the migration and transport network. This mechanism is configured to dynamically update macro-ecological parameters based on simulated changes in the concentration of characteristic components, and thereby adjust the biological interception coefficient in the migration and transport network. By integrating the migration transmission network, initial state values, and negative feedback adjustment mechanisms, a closed-loop feedback prediction model is formed.

7. The method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling according to claim 6, characterized in that, A negative feedback regulation mechanism is integrated into the migration and transport network. This mechanism is configured to dynamically update macroscopic ecological parameters based on simulated changes in characteristic component concentrations, including: If the increase in the concentration of the simulated characteristic components indicates pollution stress, then the biodiversity index and / or vegetation physiological stress index will be downgraded according to the preset ecotoxicity response relationship driven by the net increase in pollutant concentration and conforming to the exponential decay model. If the increase in the concentration of the simulated characteristic components indicates nutrient enrichment, then the vegetation physiological stress index is increased according to the preset ecological gain response relationship driven by the net increase in the concentration of beneficial nutrients and conforming to a monotonically increasing response function. The biological interception coefficient was recalculated based on the updated biodiversity index and / or vegetation physiological stress index.

8. The method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling according to claim 1, characterized in that, Based on spatiotemporal evolution scenarios and an ecological response assessment benchmark list, a quantitative map of the ecological and environmental impacts of the target area is dynamically calculated and output, including: Spatial analysis of spatiotemporal evolution scenarios is performed to extract the net change in the concentration of characteristic components for each spatial grid. Extract the land use type identifier and characteristic component attribute identifier for each spatial grid, use these as indexes to retrieve the corresponding response function in the ecological response evaluation benchmark list, substitute the net change in characteristic component concentration into the response function for function calculation, and output the ecological environment function quantity correction coefficient. The baseline ecological and environmental function quantities of each spatial grid under the corresponding land use type are extracted from the basic geographic information database, and multiplied by the ecological and environmental function quantity correction coefficient to obtain the corrected ecological and environmental function quantity accounting parameters. The system retrieves the corresponding unit environmental capacity parameters from the preset ecological and environmental carrying capacity parameter library. Combined with the corrected ecological and environmental function quantity accounting parameters, it calculates the ecological and environmental impact index of each spatial grid at each predicted time point. Then, it maps the index to the three-dimensional geographic information system according to spatial coordinates and timestamps to generate a spatial distribution layer of environmental impact that changes with time series, which serves as a quantitative map of ecological and environmental impact.

9. The method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling according to claim 8, characterized in that, The revised ecological and environmental function quantity accounting parameters are obtained, including: If the characteristic components are beneficial nutrients, then the ecological environment function quantity calculation parameters related to the ecological quality of agricultural products will be positively increased based on the ecological environment function quantity correction coefficient. If the characteristic component is a polluting heavy metal element, then the ecological and environmental function quantity calculation parameters related to water purification and biodiversity maintenance will be negatively reduced based on the ecological and environmental function quantity correction coefficient. Record the dynamic correction process of ecological environment function quantity accounting parameters and their corresponding spatiotemporal information to form a correction log.

10. The method for predicting the migration and evolution of characteristic components based on shallow water and soil isotope coupling according to claim 1, characterized in that, After dynamically calculating and outputting the quantitative map of the ecological and environmental impact of the target area, the following is also included: Spatiotemporal analysis of the quantitative map of ecological and environmental impacts was conducted to identify high-risk areas and areas where ecological quality is expected to improve; For high-risk areas, and in conjunction with migration relay paths, a set of risk intervention strategies is generated that includes engineering interception and emergency control measures; For areas where ecological quality has improved, and in conjunction with current land use data, a set of ecological efficiency enhancement strategies is generated, including suggestions for ecological restoration layout and environmental capacity regulation.

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