Method and system for simulating the whole process of migration and deposition of heterogeneous particles in a designated water area

Abstract

This disclosure relates to the fields of hydrodynamics and ecological environmental protection technology, specifically to a method and system for simulating the entire process of heterogeneous particle migration and deposition in a designated water area. The method includes: constructing a simulation model of the entire migration and deposition process using horizontal velocity interpolation functions, water depth interpolation functions, and a diffusion coefficient field established for the designated water area; under the constraint of the criterion for determining the simulated motion state of heterogeneous particles, inputting simulation data from different locations provided by the horizontal velocity interpolation function, water depth interpolation function, and diffusion coefficient field into the simulation model for training; and using the trained simulation model as the overall simulation model for the migration and deposition process in the designated water area. This disclosure fully considers the transformation mechanism of the entire process of heterogeneous particle migration and deposition (floating, suspended, deposited, and resuspended), and can efficiently and reliably simulate the entire process of heterogeneous particle migration and deposition.

Description

Technical Field

[0001] This disclosure relates to the fields of hydrodynamics and ecological environment protection technology, specifically to a method and system for simulating the entire process of heterogeneous particle migration and deposition in a designated water area. Background Technology

[0002] The migration and deposition of heterogeneous particles (such as plant seeds, fish eggs, snails, and microplastics) in rivers and lakes directly affect the ecological structure and function of rivers and lakes, and is one of the key issues in the research on river and lake ecological protection, wetland restoration, and water environment management.

[0003] Current research on particle migration and diffusion in water bodies mostly focuses on transport simulations of single particle types or single motion states (such as floating or suspended states only), and does not fully consider the influence of key environmental factors such as topographic evolution, differences in bed type, and vegetation community succession, resulting in a disconnect between simulation results and actual environmental interaction mechanisms. Furthermore, traditional particle migration simulations often use two-dimensional planar models, which are difficult to accurately depict the vertical trajectory of particles and the sedimentation and resuspension process. The sedimentation criteria are often set too simply, failing to reflect the actual impact of bed surface roughness differences and vegetation cover on particle retention.

[0004] Therefore, there is an urgent need to establish a particle migration-deposition simulation method that can reflect the entire process of heterogeneous particle motion and the mutual transformation of multiple states, and integrate the interaction between three-dimensional random motion and real environmental characteristics, so as to provide reliable basis and technical support for river and lake ecological protection, pollution source tracing and treatment, and ecological restoration decision-making. Summary of the Invention

[0005] To address the problems in related technologies, this disclosure provides a method and system for simulating the entire process of heterogeneous particle migration and deposition in a designated water area.

[0006] In a first aspect, embodiments of this disclosure provide a method for simulating the entire process of heterogeneous particle migration and deposition in a designated water area, including:

[0007] A simulation area is obtained by simulating the designated water area, and a two-dimensional hydrodynamic field model is constructed by combining the simulation area and the preset hydrodynamic conditions of the designated water area. Obtain the physical and simulated property parameters of heterogeneous particles in the specified water area; The horizontal velocity component and water depth value of each sampling point in the two-dimensional hydrodynamic field model are extracted, and a horizontal velocity interpolation function is established based on the horizontal velocity component of each sampling point, and a water depth interpolation function is established based on the water depth value of each sampling point. The roughness height field of the simulated region is determined according to the bed type of the specified water area; the frictional velocity field of the simulated region is constructed based on the horizontal velocity interpolation function, the water depth interpolation function, the roughness height field, and the logarithmic law formula; and the diffusion coefficient field of the simulated region is constructed based on the frictional velocity field. Based on the physical and simulated property parameters of the heterogeneous particles, set the corresponding simulated motion state determination conditions for the heterogeneous particles. An initial simulation model of the entire migration and deposition process is constructed using the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field. Simulation data from different locations provided by the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field are input into the initial simulation model of the entire migration and deposition process to train the initial simulation model of the entire migration and deposition process under the constraints of the simulation motion state determination conditions. Using a trained simulation model of the entire migration and deposition process, the entire migration and deposition process of the specified water area is simulated.

[0008] According to embodiments of this disclosure, simulating the designated water area to obtain a simulated region includes: An initial simulation area is determined based on the underwater topographic data of the specified water area and the preset water area distribution data; invalid areas in the initial simulation area are removed using polygon Boolean operations to obtain a simulation area with invalid areas removed; the boundary of the simulation area with invalid areas removed is smoothed by virtual points generated by the boundary normal mirroring to determine the simulated waterside boundary and the simulated bed surface boundary, thereby obtaining the final simulation area.

[0009] According to embodiments of this disclosure: The method further includes: establishing a hybrid interpolation function, wherein the hybrid interpolation function is composed of a weighted sum of a radial basis function interpolation function and a natural neighborhood interpolation function; The step of establishing a horizontal velocity interpolation function based on the horizontal velocity components of each sampling point includes: using the horizontal velocity components of each sampling point as input variables of the hybrid interpolation function to obtain the horizontal velocity interpolation function; The step of establishing a water depth interpolation function based on the water depth values ​​of each sampling point includes: using the water depth values ​​of each sampling point as input variables of the hybrid interpolation function to obtain the water depth interpolation function.

[0010] According to embodiments of this disclosure: The heterogeneous particles include one or more types; The physical property parameters include any one or more of the following: density, volume, mass, settling velocity, and buoyancy parameters of each heterogeneous particle; the simulation property parameters include any one or more of the following: source release rate, simulation duration, and simulation step size of each heterogeneous particle.

[0011] According to embodiments of this disclosure, the conditions for determining the simulated motion state of the heterogeneous particles include: First motion state determination condition: Determine the motion state of the corresponding heterogeneous particles based on the buoyancy parameters and settling velocity of the heterogeneous particles. Second motion state determination condition: When the heterogeneous particles move to the bed surface, determine whether the heterogeneous particles can be started based on the settling velocity and starting friction flow velocity of the heterogeneous particles.

[0012] According to embodiments of this disclosure, training the initial migration and deposition simulation model under the constraints of the simulated motion state determination conditions includes: Under the constraints of the simulated motion state determination conditions for each heterogeneous particle, the simulated data of different locations provided by the horizontal flow velocity interpolation function, water depth interpolation function, and diffusion coefficient are used as training data and input into the initial simulation model of the entire migration and deposition process, and the simulated motion trajectory is output. Obtain the actual motion trajectory of each heterogeneous particle; The trajectory error of the corresponding heterogeneous particles is obtained by comparing the simulated motion trajectory with the corresponding real motion trajectory. The initial simulation model of the entire migration and deposition process is adjusted according to the trajectory error until a well-trained simulation model of the entire migration and deposition process is obtained.

[0013] According to embodiments of this disclosure: The simulated motion trajectory includes simulated floating motion trajectory, simulated suspension motion trajectory, and simulated sedimentation motion trajectory; The actual motion trajectory includes the actual floating motion trajectory, the actual suspension motion trajectory, and the actual sedimentation motion trajectory.

[0014] According to embodiments of this disclosure, obtaining the true motion trajectory of each heterogeneous particle includes: The corresponding heterogeneous tracer particles are deployed upstream of the designated water area. The UAV is used to perform surface recognition and tracking of the heterogeneous tracer particles to obtain the floating heterogeneous particle distribution-time relationship of the heterogeneous tracer particles, which is used as the actual floating motion trajectory. A data acquisition device is set up at a preset water depth in the designated water area to quantitatively collect the heterogeneous tracer particles and obtain the distribution-time relationship of the suspended heterogeneous particles, which is used as the actual suspended motion trajectory. A collection device is set up on the bed surface of the designated water area to quantitatively collect the heterogeneous tracer particles and obtain the depositional heterogeneous particle distribution-time relationship of the heterogeneous tracer particles, which is used as the actual depositional movement trajectory.

[0015] According to embodiments of this disclosure, the method further includes: analyzing the full-process simulation results output by the trained migration and deposition simulation model, including: The simulated motion trajectory output by the trained simulation model of the entire migration and deposition process is projected in three layers according to the floating state, suspended state, and sedimentation state to generate a heterogeneous particle trajectory distribution map. The kernel density of the sedimentation coordinates of suspended particles in the sedimentation state is estimated, and the sedimentation probability field is output. An ecological restoration recommendation report is generated based on the heterogeneous particle trajectory distribution map and the deposition probability field.

[0016] Secondly, embodiments of this disclosure provide a simulation system for the entire process of heterogeneous particle migration and deposition in a designated water area, including: The data preprocessing module is configured to simulate the specified water area to obtain a simulation region, and to construct a two-dimensional hydrodynamic field model by combining the simulation region and the preset hydrodynamic conditions of the specified water area. The particle property determination module is configured to obtain the physical property parameters and simulated property parameters of heterogeneous particles in the specified water area; The raster data construction module is configured to extract the horizontal velocity component and water depth value of each sampling point in the two-dimensional hydrodynamic field model, establish a horizontal velocity interpolation function based on the horizontal velocity component of each sampling point, and establish a water depth interpolation function based on the water depth value of each sampling point; determine the roughness height field of the simulation area according to the bed type of the specified water area; construct the frictional velocity field of the simulation area based on the horizontal velocity interpolation function, the water depth interpolation function, the roughness height field, and the logarithmic law formula; and construct the diffusion coefficient field of the simulation area based on the frictional velocity field. The particle migration and deposition simulation module is configured to set corresponding simulation motion state determination conditions for heterogeneous particles based on their physical and simulated attribute parameters; construct an initial full-process migration and deposition simulation model using the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field; input simulation data from different locations provided by the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field into the initial full-process migration and deposition simulation model to train the initial full-process migration and deposition simulation model under the constraints of the simulated motion state determination conditions; and use the trained full-process migration and deposition simulation model to simulate the entire migration and deposition process in the specified water area.

[0017] According to embodiments of this disclosure, the system further includes: a post-processing and analysis module; The post-processing and analysis module is configured as follows: The simulated motion trajectory output by the trained simulation model of the entire migration and deposition process is projected in three layers according to the floating state, suspended state, and sedimentation state to generate a heterogeneous particle trajectory distribution map. The kernel density of the sedimentation coordinates of suspended particles in the sedimentation state is estimated, and the sedimentation probability field is output. An ecological restoration recommendation report is generated based on the heterogeneous particle trajectory distribution map and the deposition probability field.

[0018] Thirdly, embodiments of this disclosure provide a computer-readable storage medium having computer instructions stored thereon that, when executed by a processor, implement the method as described in any of the first aspects.

[0019] Fourthly, embodiments of this disclosure provide a computer program product including computer instructions that, when executed by a processor, implement the method as described in any of the first aspects.

[0020] This disclosure provides a migration and deposition simulation method that considers the transformation mechanism of the entire migration process of heterogeneous particles (floating, suspended, and sedimentary states). First, the boundary conditions, flow field, and water depth data of the simulation area are preprocessed. Then, the physical properties of the heterogeneous particles and simulation parameters are collected. Next, based on the flow field and bed surface type characteristics, the diffusion coefficient field and bed surface roughness height field are constructed, and the reasonable range of values ​​for the diffusion coefficient and roughness height is limited considering the differences in different bed surface types and particle properties. Finally, a three-dimensional random walk simulation considering the entire process of heterogeneous particle migration, settling, and resuspension is completed, including the determination of the transformation from floating to suspended states and the determination of particle deposition and resuspension criteria.

[0021] This disclosure fully considers the influence of environmental factors such as topographic evolution, differences in bed type, and vegetation community succession, and constructs a particle motion model based on three-dimensional random motion theory. This model can completely simulate the entire process of heterogeneous particle motion trajectory in river and lake systems, and accurately characterize the mutual transformation mechanism between floating, suspended, sedimented, and resuspended states, thereby comprehensively and objectively reflecting the migration and deposition characteristics of heterogeneous particles in river and lake systems.

[0022] This disclosure can also be closely integrated with ecological protection and restoration measures, providing effective support and quantitative basis for river and lake ecological governance, pollutant source tracing analysis, and the formulation of ecological restoration plans.

[0023] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0024] Other features, objects, and advantages of this disclosure will become more apparent from the following detailed description of non-limiting embodiments, taken in conjunction with the accompanying drawings. In the drawings: Figure 1 A flowchart illustrating a method for simulating the entire process of heterogeneous particle migration and deposition in a designated water area according to an embodiment of the present disclosure is shown. Figure 2 The diagram shows the horizontal velocity component cloud map of Tongjiang Oxbow Lake obtained by applying the full-process simulation method of the embodiments of this disclosure; Figure 3 A distribution map of the bed types of the Tongjiang oxbow lake obtained by applying the full-process simulation method of the embodiments of this disclosure is shown. Figure 4 The diffusion coefficient cloud map of Tongjiang oxbow lake obtained by applying the full-process simulation method of the embodiments of this disclosure is shown. Figure 5 The diagram shows the distribution of heterogeneous particle migration and deposition locations in the Tongjiang oxbow lake obtained by applying the full-process simulation method of the embodiments of this disclosure; Figure 6 A structural block diagram of a simulation system for the entire process of heterogeneous particle migration and deposition in a designated water area, according to an embodiment of the present disclosure, is shown. Detailed Implementation

[0025] In the following, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings to enable those skilled in the art to readily implement them. Furthermore, for clarity, portions unrelated to the description of exemplary embodiments have been omitted from the drawings.

[0026] In this disclosure, it should be understood that terms such as “comprising” or “having” are intended to indicate the presence of features, figures, steps, behaviors, components, parts or combinations thereof disclosed in this specification, and are not intended to exclude the possibility of the presence or addition of one or more other features, figures, steps, behaviors, components, parts or combinations thereof.

[0027] It should also be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0028] As mentioned earlier, most existing studies on particle migration and diffusion in water bodies focus on transport simulations of single particle types or single motion states (such as floating or suspended states only), and do not fully consider the influence of key environmental factors such as topographic evolution, differences in bed type, and vegetation community succession, resulting in a disconnect between simulation results and actual environmental interaction mechanisms. Furthermore, the simulations mostly use two-dimensional planar models, which are difficult to accurately depict the vertical movement trajectory and sedimentation-resuspending process of particles, and cannot reflect the actual impact of differences in bed surface roughness and vegetation cover on particle retention.

[0029] This disclosure provides a method for simulating the entire process of heterogeneous particle migration and deposition in a specified water area, including: simulating the specified water area to obtain a simulation region, and constructing a two-dimensional hydrodynamic field model by combining the simulation region and the preset hydrodynamic conditions of the specified water area; Obtain the physical and simulated property parameters of heterogeneous particles in the specified water area; The horizontal velocity component and water depth value of each sampling point in the two-dimensional hydrodynamic field model are extracted, and a horizontal velocity interpolation function is established based on the horizontal velocity component of each sampling point, and a water depth interpolation function is established based on the water depth value of each sampling point. The roughness height field of the simulated region is determined according to the bed type of the specified water area; the frictional velocity field of the simulated region is constructed based on the horizontal velocity interpolation function, the water depth interpolation function, the roughness height field, and the logarithmic law formula; and the diffusion coefficient field of the simulated region is constructed based on the frictional velocity field. Based on the physical and simulated property parameters of the heterogeneous particles, corresponding simulated motion state determination conditions for the heterogeneous particles are set; using the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field, an initial simulation model of the entire migration and deposition process is constructed; the simulated data from different locations provided by the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field are input into the initial simulation model of the entire migration and deposition process to train the initial simulation model of the entire migration and deposition process under the constraints of the simulated motion state determination conditions; using the trained simulation model of the entire migration and deposition process, the entire migration and deposition process of the specified water area is simulated.

[0030] This disclosure fully considers the influence of environmental factors such as topographic evolution and differences in bed type, and constructs a particle motion model based on three-dimensional random motion theory. This model can completely simulate the entire process of heterogeneous particle motion trajectory in river and lake systems, and accurately characterize the mutual transformation mechanism between floating, suspended, sedimented and resuspended states, thereby comprehensively and objectively reflecting the migration and deposition characteristics of heterogeneous particles in river and lake systems.

[0031] Figure 1A flowchart is shown for a method for simulating the entire process of heterogeneous particle migration and deposition in a designated water area according to an embodiment of the present disclosure; the method can be run on a computer, and more specifically, on simulation software installed on the computer.

[0032] In this disclosure, the heterogeneous particles include one or more types, such as buoyant plant seeds, microplastics, fish eggs, snails, etc. The designated water area can be various water body types such as rivers, lakes, and wetlands.

[0033] like Figure 1 As shown, the method includes the following steps S101 to S107.

[0034] In step S101, the designated water area is simulated to obtain a simulation region, and a two-dimensional hydrodynamic field model is constructed by combining the simulation region and the preset hydrodynamic conditions of the designated water area.

[0035] According to embodiments of this disclosure, simulating the designated water area to obtain a simulated region includes: An initial simulation area is determined based on the underwater topographic data of the specified water area and the preset water area distribution data; invalid areas in the initial simulation area are removed using polygon Boolean operations to obtain a simulation area with invalid areas removed; the boundary of the simulation area with invalid areas removed is smoothed by virtual points generated by the boundary normal mirroring, thereby determining the final simulation area.

[0036] In this disclosure, the underwater topographic data refers to the underwater topography, representing the elevation undulations of the underwater surface; preset water area distribution data is used to indicate how the water areas are distributed: where the water body is and where the land is. The boundary of the simulated area includes the simulated waterside boundary and the simulated bed surface boundary.

[0037] The specific implementation process is as follows: Analyze the underwater topographic data of the designated water area to obtain the elevation data of the designated water area; determine the land-water boundary according to the preset water area distribution of the designated water area; determine the simulation boundary file based on the elevation data and the land-water boundary, which is a set of vertices of a closed polygon; input the simulation boundary file into the simulation software to build the initial simulation area.

[0038] If the initial simulation boundary has self-intersections or overlaps, invalid regions in the constructed simulation region are removed using polygon Boolean operations. Finally, several virtual points are generated by mirroring the boundary along its normal direction to smooth the simulation region boundary, enhance boundary continuity, and thus obtain the final simulation region.

[0039] According to an embodiment of this disclosure, constructing a two-dimensional hydrodynamic field model by combining the preset hydrodynamic conditions of the simulated area and the designated water area includes: in the simulated area, using the preset hydrodynamic conditions as driving boundary conditions to construct a two-dimensional hydrodynamic field model to simulate the water flow distribution characteristics in the simulated area.

[0040] The hydrodynamic conditions can be set according to the type of the specified water body. For example, if the specified water body is a lake, the corresponding preset hydrodynamic condition is: inflow rate of 80 m³ / s. As another example, if the specified water body is a river, the corresponding preset hydrodynamic condition is: inflow rate of 30 m³ / s. These preset hydrodynamic conditions can be set based on user needs, literature records, or historical data. The values ​​for these preset hydrodynamic conditions are based on, but are not limited to, the following: research objectives, existing literature records, and measured historical hydrological data.

[0041] The inventors noted that the spatial resolution or data continuity of a two-dimensional hydrodynamic field model may be insufficient to meet the accuracy requirements of velocity field simulations for particle trajectory simulations. This discrepancy can lead to significant tracking errors in localized areas. Therefore, these areas need to be filled in before particle trajectory simulations to ensure the reliability of the simulation results.

[0042] Specifically, for a small number of missing or outlier points, initial imputation is performed using inverse distance weighted (IDW): ; ; Where N is the total number of sampling points involved in the interpolation calculation; For sampling point index, =1, 2, ..., N; For the first Known values ​​at the sampling point (e.g., horizontal velocity component or water depth); For the interpolation point and the first The Euclidean distance between the sampling points; This is the distance decay exponent, used to control the rate at which the weight decays with distance (usually 1-3). As weight; The interpolation result, specifically at the interpolation point. The estimated value (horizontal velocity component or water depth value).

[0043] In step S102, the physical property parameters and simulated property parameters of heterogeneous particles in the specified water area are obtained.

[0044] According to embodiments of this disclosure, the physical property parameters include: volume, mass, settling velocity, and buoyancy parameters of each heterogeneous particle.

[0045] The volume of each heterogeneous particle was obtained through on-site sampling and experimental measurement. ) and quality ( The mean value is determined using statistical methods (such as the median or geometric mean): when the physical properties of the particles follow or are close to a normal distribution, the geometric mean is preferred; when the physical properties of the particles follow or are close to a skewed distribution, the median is preferred.

[0046] The settling velocity of each type of heterogeneous particle ( It can be measured experimentally.

[0047] Among them, the settling velocity of near-spherical particles The following formula can be used for calculation: ; In the formula, The density of water, For the mass of the particles, Let V be the volume of the particle. The drag coefficient, This is the acceleration due to gravity.

[0048] Floating ability parameters ( This can be obtained by collecting sufficient samples and conducting a flotation experiment; generally, the 90th percentile is taken as the characteristic value. ).

[0049] According to embodiments of this disclosure, the simulation attribute parameters include: the number of each type of heteroparticle being simulated, the simulation duration, and the simulation step size.

[0050] The simulated number can be determined based on investigations into the sources of heterogeneous particles. For example, for plant seeds, the simulated number can be estimated by investigating and analyzing the vegetation distribution and life history characteristics within the upstream protected area.

[0051] Simulation duration ( Generally, the maximum floating time of the particle is set as a multiple (e.g., 5 to 10 times), which is the maximum time that the particle can continuously move from the moment it enters the simulation area until it finally leaves the simulation area (or sinks to the bottom). It can be adjusted appropriately according to the simulation results.

[0052] The simulation step size (Δt) needs to balance numerical stability and computational efficiency, and should be smaller than the time resolution of the flow field data and smaller than the time scale of convection.

[0053] In step S103, the horizontal velocity component and water depth value of each sampling point in the two-dimensional hydrodynamic field model are extracted, so as to establish a horizontal velocity interpolation function based on the horizontal velocity component of each sampling point and a water depth interpolation function based on the water depth value of each sampling point.

[0054] Specifically, several sampling points are set in the simulation area of ​​the two-dimensional hydrodynamic field model at a preset sampling interval, and the horizontal velocity component and water depth value of each sampling point are collected to obtain the horizontal velocity component sampling set and water depth value sampling set of the simulation area.

[0055] Furthermore, multiple sampling points were deployed in the simulated area. Collect each sampling point Horizontal velocity component at the location and water depth value The horizontal velocity component sampling set was obtained. Water depth sampling set .

[0056] The following three specific embodiments illustrate how to set up multiple sampling points in a simulated area. Those skilled in the art should understand that the method of setting up the sampling points is not intended to limit the scope of protection of this disclosure.

[0057] Example 1: It is known that when a simulation region is generated by simulation software, the simulation region is automatically divided into a uniform mesh structure (including several mesh cells).

[0058] In this disclosure, several vertical sections are defined in the simulation area based on a grid structure. The vertical section is a two-dimensional plane perpendicular to the mainstream direction of the specified water area.

[0059] Then, along the mainstream direction (e.g.) x Vertical cross-sections are laid out with a lateral spacing no greater than the grid size, ensuring that at least one cross-section passes through each grid cell. On each vertical cross-section, sampling points are laid out along the water depth direction (e.g., the h direction) with a vertical spacing no greater than half the grid resolution, ensuring that sampling points cover each vertical grid layer.

[0060] Example 2: Traverse each grid cell in the grid structure to obtain the geometric center coordinates of the grid cell, and place sampling points at the geometric center coordinates of each grid cell.

[0061] Example 3: Several vertical sections are laid out based on the underwater topographic data of the simulated area; for example, vertical sections are densely laid out in areas with large digital topographic change gradients (such as deep channels, shallow beaches, and steep slopes), and sparsely laid out in areas with relatively gentle topographic change gradients. The significance of topographic change can be quantitatively evaluated by the local slope of the digital topography.

[0062] Then, a hybrid interpolation function is constructed, which is a weighted sum of a radial basis function interpolation function and a natural neighborhood interpolation function. Specifically, this is achieved through the following formula: ; in, It is a hybrid interpolation function; It is a radial basis function interpolation function; This is the natural neighborhood interpolation function.

[0063] These are weighting coefficients used to balance the contribution ratios of the two interpolation methods; 0 ≤ ≤1, where, when When = 1, it indicates that radial basis functions are used completely, resulting in a globally smooth (gentle, continuous, and differentiable) environment, but local details may be ignored; when When = 0, it indicates that the natural neighborhood is used completely, which makes the local details more accurate but the difference result is not smooth enough; when 0 < When the value is less than 1, both global smoothness and local details can be taken into account.

[0064] For cases with large data change gradients, additional steps can be taken. To reduce local noise; for cases where the data change gradient is small, it can be reduced In order to preserve as much detail as possible.

[0065] Finally, the step of establishing a horizontal velocity interpolation function based on the horizontal velocity components of each sampling point includes: using the horizontal velocity components of each sampling point as input variables of the hybrid interpolation function to obtain the horizontal velocity interpolation function. That is, inputting the obtained horizontal velocity component sampling set into the constructed hybrid interpolation function to obtain the horizontal velocity interpolation function.

[0066] Specifically, this is achieved through the following formula: for Horizontal velocity interpolation function in the direction : ; ; ; in, For any point in the simulation area place Radial interpolation results of the horizontal velocity component; For the first sampling points The corresponding RBF weight coefficients make the RBF interpolation function at the sampling points The value at that point is equal to the horizontal velocity component at that point. N is the total number of sampling points; These are radial basis functions; For any point With the sampling point The Euclidean distance between them; For any point place Natural neighborhood interpolation results for the horizontal velocity component; For sampling points The natural neighborhood weight; For any point The set of natural neighbors of a point.

[0067] For the horizontal velocity interpolation function in the y direction : ; ; ; in, For any point in the simulation area place Radial interpolation results of the horizontal velocity component; For the first sampling points The corresponding RBF weight coefficients make the RBF interpolation function at the sampling points The value at that point is equal to the horizontal velocity component at that point. ; For any point place Natural neighborhood interpolation results for the directional horizontal velocity component.

[0068] The step of establishing a water depth interpolation function based on the water depth values ​​of each sampling point includes: using the water depth values ​​of each sampling point as input variables of the hybrid interpolation function to obtain the water depth interpolation function. That is, inputting the obtained water depth value sampling set into the constructed hybrid interpolation function to obtain the water depth interpolation function.

[0069] Specifically, this is achieved through the following formula: ; ; ; in, For any point in the simulation area Radial interpolation results of water depth at the location; For the first sampling points The corresponding RBF weight coefficients make the RBF interpolation function at the sampling points The value at that point is equal to the water depth at that point. ; for The natural neighborhood interpolation result of the water depth value at that location.

[0070] In step S104, the roughness height field of the simulated area is determined according to the bed type of the specified water area; the frictional velocity field of the simulated area is constructed based on the horizontal velocity interpolation function, the water depth interpolation function, the roughness height field, and the logarithmic law formula; and the diffusion coefficient field of the simulated area is constructed based on the frictional velocity field.

[0071] According to embodiments of this disclosure, the bed type of the designated water area determines the roughness height field of the simulated region.

[0072] The bed surface of the designated water area can be classified through on-site investigation, such as flat sand bed, gravel bed, sticky bed, vegetation cover bed, etc.

[0073] If no measured data is available, remote sensing imagery can be used to acquire red, near-infrared (NIR), and short-wave infrared (SWIR) bands, and the water index (NDWI), vegetation index (NDVI), substrate color index (SCI), and roughness Froude number (Fr) can be used. The determination is made by NDVI ≥ 0.25. In a specific embodiment, NDVI ≥ 0.25 can be determined as a vegetated bed surface; Fr <0.01% SCI ≤ 0 indicates a viscous bed; SCI ≥ 0.2% SCI ≤ 0 indicates a viscous bed. A concentration >0.05 indicates a gravel bed; other water bodies can be identified as flat sand beds.

[0074] After determining the type of bed surface, determine the corresponding roughness height based on the bed surface type: Roughness height of a leveling sand bed: The roughness height of the gravel bed: ; Roughness height of viscous bed surface: Roughness height of vegetation cover bed: ,in, For effective vegetation height, The value represents dimensionless vegetation density.

[0075] Then, a rough height field can be constructed based on the various bed types included in the specified water area. That is, the roughness height field includes roughness heights corresponding to various bed surface types.

[0076] According to an embodiment of this disclosure, the frictional velocity field of the simulated region is constructed based on the horizontal velocity interpolation function, the water depth interpolation function, the roughness height field, and the logarithmic law formula. Specifically, the horizontal velocity interpolation function, the water depth interpolation function, and the roughness height field are input into the logarithmic law formula to obtain the frictional velocity field of the simulated region.

[0077] It is known that for most natural rivers and lakes, their cross-sectional flow velocity can be approximately governed by the logarithmic law, the specific formula of which is: u ; where u water depth The horizontal velocity at the location (including horizontal velocity components in the x and y directions). Here, κ represents the frictional velocity field, and κ is the KAMAN constant. For rough height field, This represents the water depth (vertical height from the bed surface).

[0078] According to the logarithmic law formula, the frictional velocity can be calculated using the horizontal velocity component, roughness height, and water depth; that is, by interpolating the horizontal velocity in the x-direction. interpolation function of horizontal flow velocity in the y direction ， water depth interpolation function and rough height field Substituting into the logarithmic law formula, the frictional velocity field can be calculated using the following formula. ： 。

[0079] The frictional velocity field is a "thermomap" of bed surface resistance, showing where energy is being consumed rapidly and where it is relatively calm on the bed surface; specifically, it manifests as follows: The higher the value, the greater the bed surface resistance.

[0080] According to embodiments of this disclosure, the diffusion coefficient field of the simulated region is constructed based on the frictional velocity field, specifically as follows: The formula for calculating the diffusion coefficient at a certain location can be expressed as: ,in, Generally, in rivers, the longitudinal diffusion coefficient is much smaller than the velocity dispersion effect. The possible value is 0. The recommended value range is 0.1 to 0.2. The recommended value range is 0.06~0.1; in lakes, The recommended value range is 0.08 to 0.12. The suggested value is 1.5 to 2 times that, The suggested value is 1.5 to 2 times that.

[0081] Frictional flow velocity field Substituting into the diffusion coefficient calculation formula, we obtain the diffusion coefficient field: ； ； ; When considering the effects of vegetation cover and wind and wave action, an adjustment coefficient needs to be added to the distribution function of the diffusion coefficient field to obtain the following new diffusion coefficient field: ； ； ; in, For vegetation cover distribution area, This is an adjustment coefficient, typically ranging from 0.5 to 2.

[0082] In step S105, the simulated motion state determination conditions of the heterogeneous particles are set according to the physical property parameters and simulated property parameters of the heterogeneous particles.

[0083] According to embodiments of this disclosure, the conditions for determining the simulated motion state of the heterogeneous particles include: The first condition for determining the motion state is as follows: the motion state of the heterogeneous particles is determined based on their buoyancy parameters and settling velocity. That is, it is necessary to determine whether the current motion state of the heterogeneous particles is floating, suspended, or settled.

[0084] Specifically, this is achieved through the following formula: ; in, For real-time water depth changes, The simulation duration is... The real-time settling velocity of the heterogeneous particles is given.

[0085] The second motion state determination condition is as follows: When the heterogeneous particles move to the bed surface, it is determined whether the heterogeneous particles can be started based on their settling velocity and initiation friction velocity. That is, for heterogeneous particles that have touched the bed surface, it is necessary to determine whether they can be started again. For example, the initiation friction velocity threshold of the particles is determined according to the type of bed surface. When the local hydrodynamic conditions do not meet the initiation requirements (e.g., 1.1 cm / s), the particles will settle; otherwise, the particles will be started again.

[0086] Specifically, this is achieved through the following formula: ; in, The frictional velocity threshold of the heterogeneous particles is the minimum frictional velocity required for the particles on the bed surface to change from a static state to a dynamic state. The real-time frictional velocity of the heterogeneous particles is given.

[0087] In step S106, an initial simulation model of the entire migration and deposition process is constructed using the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field. Simulation data from different locations provided by the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field are input into the initial simulation model of the entire migration and deposition process to train the initial simulation model of the entire migration and deposition process under the constraints of the simulation motion state determination conditions.

[0088] In step S107, the migration and deposition process of the specified water area is simulated using a trained simulation model.

[0089] In this disclosure, the core of the simulation model for the entire migration and deposition process is the equation for simulating the motion trajectory, which is expressed by the following formula: ; in, , , These are independent standard normal random numbers. for The simulated motion trajectory in the direction, for The simulated motion trajectory in the direction, for The simulated motion trajectory in the direction, for Diffusion coefficient field in the direction, for Diffusion coefficient field in the direction, for Diffusion coefficient field in the direction, For the settling velocity, This is to simulate the step size.

[0090] That is, according to direction and Horizontal velocity interpolation function in the direction , ,as well as , , Diffusion coefficient field in the direction , , This allows for the construction of a simulated motion trajectory equation for a three-dimensional random walk of the heterogeneous particles. During the particle motion simulation, the position update of each trajectory step employs random walk theory. Specifically, the particle displacement is decomposed into a deterministic drift term and a random diffusion term, which are superimposed to form a "drift-diffusion" type random walk process. That is, the particle's position update at each time step is controlled by the average flow field and also superimposed with random perturbations conforming to a Gaussian distribution.

[0091] This disclosure innovatively considers the state transformation mechanism of particles from floating to suspended state (with a floating time threshold set) and from suspended state to sedimentary state (settling and initiation), which significantly improves the accuracy and ecological effectiveness of the simulation results.

[0092] According to embodiments of this disclosure, the model training process includes: Under the constraints of the simulated motion state determination conditions for each heterogeneous particle, the simulated data of different locations provided by the horizontal flow velocity interpolation function, water depth interpolation function, and diffusion coefficient are used as training data and input into the initial simulation model of the entire migration and deposition process, and the simulated motion trajectory is output. Obtain the actual motion trajectory of each heterogeneous particle; The trajectory error of the corresponding heterogeneous particles is obtained by comparing the simulated motion trajectory with the corresponding real motion trajectory. The initial simulation model of the entire migration and deposition process is adjusted according to the trajectory error until a well-trained simulation model of the entire migration and deposition process is obtained.

[0093] According to embodiments of this disclosure, the simulated motion trajectory includes a simulated floating motion trajectory, a simulated suspending motion trajectory, and a simulated sedimentation motion trajectory; the real motion trajectory includes a real floating motion trajectory, a real suspending motion trajectory, and a real sedimentation motion trajectory.

[0094] According to embodiments of this disclosure, obtaining the true motion trajectory of each heterogeneous particle includes: The corresponding heterogeneous tracer particles are deployed upstream of the designated water area. The UAV is used to perform surface recognition and tracking of the heterogeneous tracer particles to obtain the floating heterogeneous particle distribution-time relationship of the heterogeneous tracer particles, which is used as the actual floating motion trajectory. A data acquisition device is set up at a preset water depth in the designated water area to quantitatively collect the heterogeneous tracer particles and obtain the distribution-time relationship of the suspended heterogeneous particles, which is used as the actual suspended motion trajectory. A collection device is set up on the bed surface of the designated water area to quantitatively collect the heterogeneous tracer particles and obtain the depositional heterogeneous particle distribution-time relationship of the heterogeneous tracer particles, which is used as the actual depositional movement trajectory.

[0095] In this disclosure, the acquisition device includes an optical fiber fluorescence-turbidity collector and a recyclable magnetic trapping net.

[0096] The following specific embodiment illustrates how to obtain the actual floating trajectory, actual suspension trajectory, and actual deposition trajectory of the heterogeneous particles: Fluorescent-RFID heterogeneous tracer particles of known spectrum were simultaneously released upstream of the simulated water area. Unmanned aerial vehicle (UAV) night-flying spectral imaging was used to identify and track floating particles in a specific area, obtaining the distribution-time relationship of the floating heterogeneous particles. Fiber optic fluorescence-turbidity collectors and retrievable magnetic capture nets were deployed at three water depths to quantitatively collect suspended particles, obtaining the distribution-time relationship of suspended heterogeneous particles. Fiber optic fluorescence-turbidity collectors and retrievable magnetic capture nets were deployed on the bed surface to quantitatively collect sedimentary particles, obtaining the distribution-time relationship of sedimentary heterogeneous particles.

[0097] In this disclosure, the distribution-time relationship of heterogeneous particles in floating, suspended and sedimented states is used as a real control group for the model output to obtain floating trajectory error, suspended concentration error and sedimentation ratio error. The simulation accuracy of the model is analyzed and the model parameters are automatically updated based on sensitivity analysis to obtain the final model.

[0098] According to embodiments of this disclosure, the method further includes: analyzing the full-process simulation results output by the trained migration and deposition simulation model, including: projecting the simulated motion trajectory output by the trained migration and deposition simulation model into three layers according to floating state, suspended state, and sedimentation state to generate a heterogeneous particle trajectory distribution map; estimating the kernel density of the sedimentation coordinates of suspended particles in the sedimentation state to output a sedimentation probability field; and generating an ecological restoration recommendation report based on the heterogeneous particle trajectory distribution map and the sedimentation probability field.

[0099] In one specific implementation, the simulated motion trajectory output by the model is projected in three layers according to the floating state, suspended state, and sedimented state to generate a heterogeneous particle trajectory distribution map; in another specific implementation, the simulated motion trajectory is further divided into floating (0 m), suspended (0 m), and sedimented (0 m) states. 0.5 That is, the position 0.5 times the water depth below the water surface), sediment (which can be near the bed, for example...). 0.9 Three layers of projection were performed at the location 0.9 times the water depth below the surface, and a 10 cm isobath and a current velocity map were superimposed to generate the heterogeneous particle trajectory distribution at different time points.

[0100] The kernel density of suspended particles in the depositional state is estimated, and the depositional probability field is output. Specifically, this includes: For suspended particles in the depositional state, their depositional coordinates are extracted and uniformly mapped to the analysis grid. Based on this, the kernel density of each depositional coordinate is estimated to obtain the depositional probability field characterizing the spatial distribution characteristics of particle deposition. Then, based on the cumulative distribution of the depositional probability field, the main depositional region containing 95% of the total depositional probability is determined and its area is calculated to characterize the main influence range of deposition. At the same time, the maximum local density (to reflect the local accumulation intensity), skewness coefficient (to characterize the skewness and concentration of the depositional density distribution), and component uniformity index (to characterize the uniformity of the distribution of particles of different sizes, densities, or types in the depositional region) are calculated in combination with the particle deposition amount in each grid.

[0101] Based on the heterogeneous particle trajectory distribution map and the sedimentation probability field, ecological restoration suggestions are generated. For example, by overlaying the sedimentation probability field with the multi-year vegetation succession map, the "sedimentation promotion zone" (deposition increase - vegetation expansion) and the "sedimentation retardation zone" (deposition decrease - vegetation shrinkage) can be analyzed and identified.

[0102] According to embodiments of this disclosure, the analysis may further include: based on different bed types, statistically analyzing the heterogeneous particle deposition rate (number of deposited particles / number of deployed particles) under different bed types, and analyzing the retention rate change of heterogeneous particles in the bed transition zone, wherein the bed transition zone is the transition area between different bed types in the simulation area.

[0103] Finally, post-processing methods for the simulation results are presented, including methods for visualizing the spatial distribution of particles and statistical analysis of sedimentary distribution characteristics. By integrating environmental factors such as topographic change, bed type and vegetation succession, suggestions are made on methods for coupled analysis of heterogeneous particle deposition and ecological factors and suggestions for ecological restoration strategies.

[0104] This disclosure eliminates the need for complex on-site data collection, enabling efficient and reliable simulation of the entire process of heterogeneous particles. It comprehensively reflects the migration and deposition characteristics of heterogeneous particles in river and lake systems, fully revealing their migration and deposition patterns, and possesses significant value for ecological restoration applications. Furthermore, it can be closely integrated with ecological protection and restoration measures, providing effective support and quantitative basis for river and lake ecological governance, pollutant source tracing analysis, and the formulation of ecological restoration plans.

[0105] The following example uses a typical oxbow lake connected to the Yangtze River in the middle reaches of the Yangtze River, combined with... Figure 2 - Figure 5 The simulation method for the entire process of heterogeneous particle migration and deposition according to embodiments of this disclosure is described.

[0106] Figure 2 The diagram shows a cloud map of the horizontal velocity component of the Tongjiang oxbow lake obtained by applying the full-process simulation method of the embodiments of this disclosure.

[0107] like Figure 2 As shown in the figure, this map displays the Tongjiang Oxbow Lake. direction and The spatial distribution characteristics of the horizontal velocity component along the direction of the main flow are shown in the figure. High velocity areas are mainly distributed in narrow terrain, suggesting that these are locations where the main flow is accelerated due to topographic constraints. The velocity distribution along the main flow direction exhibits a "low-high-low" pattern, reflecting a typical pattern of velocity distribution in the river cross-section: higher velocity in the main flow area and lower velocity near the bank. The velocity on the left side approaches zero, possibly corresponding to a still water area or backflow zone near the bank.

[0108] Figure 3 The diagram shows the distribution of bed types of the Tongjiang oxbow lake obtained by applying the full-process simulation method of the embodiments of this disclosure.

[0109] like Figure 3 As shown in the figure, this diagram illustrates the spatial distribution pattern of three bed types—silt, gravel, and vegetation—in Tongjiang Oxbow Lake. Areas near the lake shore are primarily covered by vegetation; areas adjacent to vegetation are dominated by gravel beds; and the central area of ​​the lake, due to weaker hydrodynamics, becomes the main depositional zone for silt.

[0110] Figure 4 The diagram shows the diffusion coefficient cloud map of Tongjiang Oxbow Lake obtained by applying the full-process simulation method of the embodiments of this disclosure.

[0111] like Figure 4 As shown, the diffusion coefficient exhibits significant spatial heterogeneity: the diffusion coefficient in the nearshore vegetation-covered area approaches zero; the diffusion coefficient gradually increases towards the lake center transition area; and the diffusion coefficient reaches its highest value in the central area of ​​the lake center (corresponding to the silty bed surface), reflecting strong water turbulence and active material exchange in this area.

[0112] Figure 5 The diagram shows the distribution of heterogeneous particle migration and deposition locations in Tongjiang Oxbow Lake obtained by applying the full-process simulation method of the embodiments of this disclosure.

[0113] like Figure 5 As shown, the particle distribution at simulation times t=500s, 1500s, 2500s, and 3000s is displayed. The background in the figure represents the water depth field, and the superimposed points represent particle locations.

[0114] Therefore, at t=500s, the particles are mainly concentrated near the release area; as time goes on, the particles gradually spread to the entire water area and are deposited in the shallow water area near the shore; by t=3000s, the particle distribution tends to be stable, and the deposition area is mainly concentrated in the outer periphery of the lake center and the nearshore zone, reflecting the control of hydrodynamic conditions and bed type on particle fate.

[0115] Figure 6 A structural block diagram of a simulation system for the entire process of heterogeneous particle migration and deposition in a designated water area, according to an embodiment of the present disclosure, is shown.

[0116] like Figure 6 As shown, the system 600 includes a data preprocessing module 610, a particle attribute determination module 620, a raster data construction module 630, and a particle migration and deposition simulation module 640.

[0117] The data preprocessing module 610 is configured to simulate the designated water area to obtain a simulation region, and to construct a two-dimensional hydrodynamic field model by combining the simulation region and the preset hydrodynamic conditions of the designated water area. The particle property determination module 620 is configured to obtain the physical property parameters and simulated property parameters of heterogeneous particles in the specified water area; The raster data construction module 630 is configured to extract the horizontal velocity component and water depth value of each sampling point in the two-dimensional hydrodynamic field model, to establish a horizontal velocity interpolation function based on the horizontal velocity component of each sampling point, and to establish a water depth interpolation function based on the water depth value of each sampling point; to determine the roughness height field of the simulation area based on the bed type of the specified water area; to construct the frictional velocity field of the simulation area based on the horizontal velocity interpolation function, the water depth interpolation function, the roughness height field, and the logarithmic law formula; and to construct the diffusion coefficient field of the simulation area based on the frictional velocity field. The particle migration and deposition simulation module 640 is configured to set corresponding simulation motion state determination conditions for heterogeneous particles based on their physical and simulated attribute parameters; construct an initial migration and deposition full-process simulation model using the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field; input simulation data from different locations provided by the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field into the initial migration and deposition full-process simulation model to train the initial migration and deposition full-process simulation model under the constraints of the simulated motion state determination conditions; and use the trained migration and deposition full-process simulation model to simulate the entire migration and deposition process in the specified water area.

[0118] According to an embodiment of this disclosure, the system 600 further includes: a post-processing and analysis module 650; The post-processing and analysis module 650 is configured to: project the simulated motion trajectory output by the trained migration and deposition simulation model into three layers according to the floating state, suspended state, and sedimentation state to generate a heterogeneous particle trajectory distribution map; estimate the kernel density of the sedimentation coordinates of suspended particles in the sedimentation state to output a sedimentation probability field; and generate an ecological restoration suggestion report based on the heterogeneous particle trajectory distribution map and the sedimentation probability field.

[0119] In particular, according to embodiments of this disclosure, the methods described above can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program containing program code for performing the methods described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium.

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0121] The units or modules described in the embodiments of this disclosure can be implemented in software or programmable hardware. The described units or modules can also be located in a processor, and the names of these units or modules do not necessarily constitute a limitation on the unit or module itself.

[0122] In another aspect, this disclosure also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or computer system described above; or it may be a standalone computer-readable storage medium not assembled into a device. The computer-readable storage medium stores one or more programs, which are used by one or more processors to perform the methods described in this disclosure.

[0123] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

Claims

1. A method for simulating the entire process of heterogeneous particle migration and deposition in a specified water area, characterized in that, include: A simulation area is obtained by simulating the designated water area, and a two-dimensional hydrodynamic field model is constructed by combining the simulation area and the preset hydrodynamic conditions of the designated water area. Obtain the physical and simulated property parameters of heterogeneous particles in the specified water area; The horizontal velocity component and water depth value of each sampling point in the two-dimensional hydrodynamic field model are extracted, and a horizontal velocity interpolation function is established based on the horizontal velocity component of each sampling point, and a water depth interpolation function is established based on the water depth value of each sampling point. The roughness height field of the simulated region is determined according to the bed type of the specified water area; the frictional velocity field of the simulated region is constructed based on the horizontal velocity interpolation function, the water depth interpolation function, the roughness height field, and the logarithmic law formula; and the diffusion coefficient field of the simulated region is constructed based on the frictional velocity field. Based on the physical and simulated property parameters of the heterogeneous particles, set the corresponding simulated motion state determination conditions for the heterogeneous particles. An initial simulation model of the entire migration and deposition process is constructed using the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field. Simulation data from different locations provided by the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field are input into the initial simulation model of the entire migration and deposition process to train the initial simulation model of the entire migration and deposition process under the constraints of the simulation motion state determination conditions. Using a trained simulation model of the entire migration and deposition process, the entire migration and deposition process of the specified water area is simulated.

2. The simulation method according to claim 1, characterized in that, The process of simulating the designated water area to obtain the simulated region includes: An initial simulation area is determined based on the underwater topographic data of the specified water area and the preset water area distribution data; invalid areas in the initial simulation area are removed using polygon Boolean operations to obtain a simulation area with invalid areas removed; the boundary of the simulation area with invalid areas removed is smoothed by virtual points generated by the boundary normal mirroring to determine the simulated waterside boundary and the simulated bed surface boundary, thereby obtaining the final simulation area.

3. The simulation method according to claim 1, characterized in that: The method further includes: establishing a hybrid interpolation function, wherein the hybrid interpolation function is composed of a weighted sum of a radial basis function interpolation function and a natural neighborhood interpolation function; The step of establishing a horizontal velocity interpolation function based on the horizontal velocity components of each sampling point includes: using the horizontal velocity components of each sampling point as input variables of the hybrid interpolation function to obtain the horizontal velocity interpolation function; The step of establishing a water depth interpolation function based on the water depth values ​​of each sampling point includes: using the water depth values ​​of each sampling point as input variables of the hybrid interpolation function to obtain the water depth interpolation function.

4. The simulation method according to claim 1, characterized in that: The heterogeneous particles include one or more types; The physical property parameters include any one or more of the following: density, volume, mass, settling velocity, and buoyancy parameters of each heterogeneous particle; the simulation property parameters include any one or more of the following: source release rate, simulation duration, and simulation step size of each heterogeneous particle.

5. The simulation method according to claim 4, characterized in that, The conditions for determining the simulated motion state of the heterogeneous particles include: First motion state determination condition: Determine the motion state of the corresponding heterogeneous particles based on the buoyancy parameters and settling velocity of the heterogeneous particles. Second motion state determination condition: When the heterogeneous particles move to the bed surface, determine whether the heterogeneous particles can be started based on the settling velocity and starting friction flow velocity of the heterogeneous particles.

6. The simulation method according to claim 5, characterized in that, The training of the initial migration and deposition simulation model under the constraints of the simulated motion state determination conditions includes: Under the constraints of the simulated motion state determination conditions for each heterogeneous particle, the simulated data of different locations provided by the horizontal flow velocity interpolation function, water depth interpolation function, and diffusion coefficient are used as training data and input into the initial simulation model of the entire migration and deposition process, and the simulated motion trajectory is output. Obtain the actual motion trajectory of each heterogeneous particle; The trajectory error of the corresponding heterogeneous particles is obtained by comparing the simulated motion trajectory with the corresponding real motion trajectory. The initial simulation model of the entire migration and deposition process is adjusted according to the trajectory error until a well-trained simulation model of the entire migration and deposition process is obtained.

7. The simulation method according to claim 6, characterized in that: The simulated motion trajectory includes simulated floating motion trajectory, simulated suspension motion trajectory, and simulated sedimentation motion trajectory; The actual motion trajectory includes the actual floating motion trajectory, the actual suspension motion trajectory, and the actual sedimentation motion trajectory.

8. The simulation method according to claim 7, characterized in that, The acquisition of the true motion trajectory of each heterogeneous particle includes: The corresponding heterogeneous tracer particles are deployed upstream of the designated water area. The UAV is used to perform surface recognition and tracking of the heterogeneous tracer particles to obtain the floating heterogeneous particle distribution-time relationship of the heterogeneous tracer particles, which is used as the actual floating motion trajectory. A data collection device is set up at a preset water depth in the designated water area to quantitatively collect the heterogeneous tracer particles and obtain the distribution-time relationship of the suspended heterogeneous particles, which is used as the actual suspended motion trajectory. A collection device is set up on the bed surface of the designated water area to quantitatively collect the heterogeneous tracer particles and obtain the depositional heterogeneous particle distribution-time relationship of the heterogeneous tracer particles, which is used as the actual depositional movement trajectory.

9. The simulation method according to claim 1, characterized in that, The method further includes: analyzing the full-process simulation results output by the trained migration and deposition simulation model, including: The simulated motion trajectory output by the trained simulation model of the entire migration and deposition process is projected in three layers according to the floating state, suspended state, and sedimentation state to generate a heterogeneous particle trajectory distribution map. The kernel density of the sedimentation coordinates of suspended particles in the sedimentation state is estimated, and the sedimentation probability field is output. An ecological restoration recommendation report is generated based on the heterogeneous particle trajectory distribution map and the deposition probability field.

10. A simulation system for the entire process of heterogeneous particle migration and deposition in a specified water area, characterized in that, include: The data preprocessing module is configured to simulate the specified water area to obtain a simulation region, and to construct a two-dimensional hydrodynamic field model by combining the simulation region and the preset hydrodynamic conditions of the specified water area. The particle property determination module is configured to obtain the physical property parameters and simulated property parameters of heterogeneous particles in the specified water area; The raster data construction module is configured to extract the horizontal velocity component and water depth value of each sampling point in the two-dimensional hydrodynamic field model, establish a horizontal velocity interpolation function based on the horizontal velocity component of each sampling point, and establish a water depth interpolation function based on the water depth value of each sampling point; determine the roughness height field of the simulation area according to the bed type of the specified water area; construct the frictional velocity field of the simulation area based on the horizontal velocity interpolation function, the water depth interpolation function, the roughness height field, and the logarithmic law formula; and construct the diffusion coefficient field of the simulation area based on the frictional velocity field. The particle migration and deposition simulation module is configured to set corresponding simulation motion state determination conditions for heterogeneous particles based on their physical and simulated attribute parameters; construct an initial full-process migration and deposition simulation model using the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field; input simulation data from different locations provided by the horizontal velocity interpolation function, the water depth interpolation function, and the diffusion coefficient field into the initial full-process migration and deposition simulation model to train the initial full-process migration and deposition simulation model under the constraints of the simulated motion state determination conditions; and use the trained full-process migration and deposition simulation model to simulate the entire migration and deposition process in the specified water area.

11. The system according to claim 10, characterized in that, The system also includes: a post-processing and analysis module; The post-processing and analysis module is configured as follows: The simulated motion trajectory output by the trained simulation model of the entire migration and deposition process is projected in three layers according to the floating state, suspended state, and sedimentation state to generate a heterogeneous particle trajectory distribution map. The kernel density of the sedimentation coordinates of suspended particles in the sedimentation state is estimated, and the sedimentation probability field is output. An ecological restoration recommendation report is generated based on the heterogeneous particle trajectory distribution map and the deposition probability field.

12. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, they implement the method of any one of claims 1 to 9.

13. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the method of any one of claims 1 to 9.

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